Showing GIS and Remote Sensing Application in flood disaster management. Source: Authors’ Construct with reference to [44].
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"6395",leadTitle:null,fullTitle:"Bridge Engineering",title:"Bridge Engineering",subtitle:null,reviewType:"peer-reviewed",abstract:"A bridge is a structure built to span the physical obstacles without closing the way underneath, such as a body of water, valley, or road, for the purpose of providing the passage over the obstacle. Bridge engineering is an engineering discipline branching from civil engineering that involves the planning, design, construction, operation, and maintenance of bridges to ensure safe and effective transportation of vehicles, people and goods. This book Bridge Engineering includes the main topics and the basic principles of bridge engineering and provides the full scope of current information necessary for effective and cost-conscious contemporary bridge. It reflects new engineering and building developments, the most current design methods, and the latest industry standards and policies. It provides a comprehensive overview of the significant characteristics for bridge engineering. It highlights the recent advancements, requirements, improvements, and details of the latest techniques in the global market. It contains a collection of the latest research developments on the bridge engineering. It comprehensively covers the basic theory and practice in sufficient depth to provide a solid grounding to bridge engineers. It helps readers to maximize effectiveness in all facets of bridge engineering. This professional book as a credible source and a valuable reference can be very applicable and useful for all professors, researchers, engineers, practicing professionals, trainee practitioners, students and others who are interested in the bridge projects.",isbn:"978-1-78923-105-2",printIsbn:"978-1-78923-104-5",pdfIsbn:"978-1-83881-451-9",doi:"10.5772/intechopen.70024",price:119,priceEur:129,priceUsd:155,slug:"bridge-engineering",numberOfPages:150,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"1d5fcf0ef5708024ef95eb8b3d7310be",bookSignature:"Hamid Yaghoubi",publishedDate:"May 23rd 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6395.jpg",numberOfDownloads:18962,numberOfWosCitations:7,numberOfCrossrefCitations:6,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:9,numberOfDimensionsCitationsByBook:1,hasAltmetrics:0,numberOfTotalCitations:22,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 8th 2017",dateEndSecondStepPublish:"June 29th 2017",dateEndThirdStepPublish:"September 25th 2017",dateEndFourthStepPublish:"December 24th 2017",dateEndFifthStepPublish:"February 22nd 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"103965",title:"Dr.",name:"Hamid",middleName:null,surname:"Yaghoubi",slug:"hamid-yaghoubi",fullName:"Hamid Yaghoubi",profilePictureURL:"https://mts.intechopen.com/storage/users/103965/images/system/103965.jpeg",biography:"Dr. Hamid Yaghoubi is the director of Iran Maglev Technology (IMT). He became the Iran top researcher in 2010. In this regard, he was awarded by the Iranian president; the Iranian Minister of Science, Research and Technology; and the Iranian Minister of Information and Communication Technology. He became the 2011 and 2012 Outstanding Reviewer for the Journal of Transportation Engineering (JTE), American Society of Civil Engineers (ASCE), USA. One of his journal papers became the 2011 Top Download Paper for JTE. He received the ICCTP2011 Award for the 11th International Conference of Chinese Transportation Professionals (ICCTP2011), ASCE. He is an assistant chief editor and an editorial board member for some journals. He has been a reviewer for the majority of journals, books and conferences. He has also been an editor for some books. 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The proposed bridge design has a total span of 4440 m with two 330-m end spans and a central span of 3780 m. The height of the two pylons is 702 m, and the deck width is 40 m. The features of this structure include the combination of a suspension bridge and cable-stayed bridge, application of carbon fibre materials, extension of deck width and pretension techniques. Linear static analysis, dynamic analysis and theoretical analysis are conducted under different loading cases. In linear static analysis, the stresses under critical load combinations are smaller than the ultimate strength of the materials. However, the maximum deflection under the dead and wind load combination exceeds the specified serviceability limit.",signatures:"Faham Tahmasebinia, Samad Mohammad Ebrahimzadeh\nSepasgozar, Hannah Blum, Kakarla Raghava Reddy, Fernando\nAlonso-Marroquin, Qile Gao, Yang Hu, Xu Wang and Zhongzheng\nWang",downloadPdfUrl:"/chapter/pdf-download/60236",previewPdfUrl:"/chapter/pdf-preview/60236",authors:[{id:"211659",title:"Dr.",name:"Faham",surname:"Tahmasebinia",slug:"faham-tahmasebinia",fullName:"Faham Tahmasebinia"},{id:"221172",title:"Dr.",name:"Samad M.E.",surname:"Sepasgozar",slug:"samad-m.e.-sepasgozar",fullName:"Samad M.E. Sepasgozar"}],corrections:null},{id:"57609",title:"Developing a Bridge Condition Rating Model Based on Limited Number of Data Sets",doi:"10.5772/intechopen.71556",slug:"developing-a-bridge-condition-rating-model-based-on-limited-number-of-data-sets",totalDownloads:1146,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"This chapter utilizes artificial neural network (ANN) and multiple regression analysis (MRA) to model bridge condition rating based on limited number of data sets. Since data sets are very limited and there is a gap in range of rating scale, two conditions of data sets are used in this study, namely complete data sets and data set with bridge component condition rating data are missing. Five methods are then used to handle the missing bridge component condition rating data. Three commonly used methods and two new methods are explored in this study. It seems that the performance of the model using data sets after handling missing bridge component data to fill the gaps in the range scales of the bridge condition rating improved the performance of the model. In addition, a handling method that substitutes missing data of bridge component ratings with available bridge rating data is favorable. Based on the values of root mean square error (RMSE) and R2, the ANN models perform slightly better than MRA to map relationship between bridge components and bridge condition rating. This concluded that ANN is suitable to model bridge condition rating compare to MRA method.",signatures:"Khairullah Yusuf and Roszilah Hamid",downloadPdfUrl:"/chapter/pdf-download/57609",previewPdfUrl:"/chapter/pdf-preview/57609",authors:[{id:"180233",title:"Associate Prof.",name:"Roszilah",surname:"Hamid",slug:"roszilah-hamid",fullName:"Roszilah Hamid"},{id:"216888",title:"Dr.",name:"Khairullah",surname:"Yusof",slug:"khairullah-yusof",fullName:"Khairullah Yusof"}],corrections:null},{id:"57516",title:"Structural Identification (St-Id) Concept for Performance Prediction of Long-Span Bridges",doi:"10.5772/intechopen.71558",slug:"structural-identification-st-id-concept-for-performance-prediction-of-long-span-bridges",totalDownloads:1369,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Long-span cable-supported bridges are the lifeline structures for the transportation network in a country/state. An effective solution of this type of bridges is therefore indispensable not only to better understand structural response of them but also to conduct an efficient maintenance and management strategy for these bridges. In this study, structural identification (St-Id) is implemented to estimate the performance of the Bosphorus Bridge. In addition, certain efforts from finite element modeling (FEM) to utilization for performance prediction are given based on each step of St-Id. St-Id concept is divided into two main parts: experimental and numerical investigations. Due to the high cost and time limitation for testing of long-span bridges, the most effective solution to the experimental research is SHM system (SHMs). For this purpose, the SHMs of the Bosphorus Bridge is considered, finite element modeling provides an extended solution from analysis to model updating of the bridges. Considering structural performance of the bridge under extreme wind load and multi-point earthquake motion is estimated. The results from the current study indicate that St-Id concept is a robust approach for overall structural condition assessment and performance prediction of long-span cable-supported bridges.",signatures:"Selcuk Bas",downloadPdfUrl:"/chapter/pdf-download/57516",previewPdfUrl:"/chapter/pdf-preview/57516",authors:[{id:"213939",title:"Dr.",name:"Selcuk",surname:"Bas",slug:"selcuk-bas",fullName:"Selcuk Bas"}],corrections:null},{id:"58008",title:"Recent Advances in the Serviceability Assessment of Footbridges Under Pedestrian-Induced Vibrations",doi:"10.5772/intechopen.71888",slug:"recent-advances-in-the-serviceability-assessment-of-footbridges-under-pedestrian-induced-vibrations",totalDownloads:1139,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Current international guidelines determine the effect of pedestrians on footbridges via an equivalent harmonic load. However, the dynamic response of footbridges obtained according to these standards differs from the values recorded experimentally. In order to overcome this issue, a new modelling framework has been recently proposed by several researchers. This novel approach allows considering more accurately three key aspects: (i) the inter- and intra-subject variability, (ii) the pedestrian-structure interaction and (iii) the crowd dynamics. For this purpose, different crowd-structure interaction models have been developed. Despite the large number of proposals, all of them share the same scheme: the crowd-structure interaction is simulated by linking two sub-models, namely (i) a pedestrian-structure interaction sub-model and (ii) a crowd sub-model. Furthermore, the variability of the pedestrian’s behaviour may be taken into account via the assumption that the model parameters are random variables. In this chapter, a summary of the state-of-art of this new modelling framework is presented, with special emphasis in a case study where the crowd-structure interaction model developed by the authors is used to simulate the lateral lock-in phenomenon on a real footbridge.",signatures:"Javier Fernando Jiménez-Alonso and Andrés Sáez",downloadPdfUrl:"/chapter/pdf-download/58008",previewPdfUrl:"/chapter/pdf-preview/58008",authors:[{id:"215797",title:"Ph.D.",name:"Javier Fernando",surname:"Jiménez-Alonso",slug:"javier-fernando-jimenez-alonso",fullName:"Javier Fernando Jiménez-Alonso"},{id:"215798",title:"Prof.",name:"Andres",surname:"Saez",slug:"andres-saez",fullName:"Andres Saez"}],corrections:null},{id:"58853",title:"Wind Action Phenomena Associated with Large-Span Bridges",doi:"10.5772/intechopen.73061",slug:"wind-action-phenomena-associated-with-large-span-bridges",totalDownloads:10273,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In the past, the design of bridges over increasing distances was limited by construction techniques and, as always, by economics. As technological advances have turned possible cable-supported bridges of incredible spans, a new challenge has been added to the equation: that of withstanding the action of winds without developing undesirable dynamic responses. In this chapter, the several aerodynamic phenomena of relevance to long-span bridges are classified and discussed. This will interest both experts and non-experts in the field, thanks to the overview that is given. For certain cases, codes of practice recommend wind tunnel tests. The reader is introduced to these, as well as to numerical simulations, which are currently gaining increasing importance. Next, measures for attenuating susceptibility for undesirable dynamic responses are reviewed. The chapter ends with a discussion of the Vila Real Bridge deck section, based on wind tunnel tests and numerical simulations carried out by the authors: the aerodynamics was effectively improved with geometrically subtle modifications that were proposed and adopted still in the design phase.",signatures:"Daniel C. Vaz, Raquel A.B. Almeida and António R. Janeiro Borges",downloadPdfUrl:"/chapter/pdf-download/58853",previewPdfUrl:"/chapter/pdf-preview/58853",authors:[{id:"60072",title:"Prof.",name:"Raquel",surname:"Almeida",slug:"raquel-almeida",fullName:"Raquel Almeida"},{id:"216824",title:"Prof.",name:"Daniel",surname:"Vaz",slug:"daniel-vaz",fullName:"Daniel Vaz"},{id:"216827",title:"Prof.",name:"A.R.",surname:"Janeiro Borges",slug:"a.r.-janeiro-borges",fullName:"A.R. Janeiro Borges"}],corrections:null},{id:"59297",title:"Bridges Subjected to Dynamic Loading",doi:"10.5772/intechopen.73193",slug:"bridges-subjected-to-dynamic-loading",totalDownloads:2077,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter reviews the analysis of problems of highway and rail bridge dynamic response to moving traffic loads. Bridge vibrations analyses comprise solution of many interdisciplinary problems. During the two last centuries, these problems have been studied by theoretical, numerical and experimental way by many investigators. Therefore, the present chapter contains only the basic approaches for solving the complex problem of bridges subjected to dynamic loading.",signatures:"Ján Benčat and Robert Kohár",downloadPdfUrl:"/chapter/pdf-download/59297",previewPdfUrl:"/chapter/pdf-preview/59297",authors:[{id:"216765",title:"Prof.",name:"Ján",surname:"Benčat",slug:"jan-bencat",fullName:"Ján Benčat"},{id:"235614",title:"Associate Prof.",name:"Robert",surname:"Kohar",slug:"robert-kohar",fullName:"Robert Kohar"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:[{id:"65",label:"highly cited contributor"}]},relatedBooks:[{type:"book",id:"5423",title:"Urban Transport Systems",subtitle:null,isOpenForSubmission:!1,hash:"222b5d90a7014dbff7e33f3dcde6bc1d",slug:"urban-transport-systems",bookSignature:"Hamid Yaghoubi",coverURL:"https://cdn.intechopen.com/books/images_new/5423.jpg",editedByType:"Edited by",editors:[{id:"103965",title:"Dr.",name:"Hamid",surname:"Yaghoubi",slug:"hamid-yaghoubi",fullName:"Hamid Yaghoubi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6103",title:"Highway Engineering",subtitle:null,isOpenForSubmission:!1,hash:"9c66d18cec90a84fdfd9a64451dc421a",slug:"highway-engineering",bookSignature:"Hamid Yaghoubi",coverURL:"https://cdn.intechopen.com/books/images_new/6103.jpg",editedByType:"Edited by",editors:[{id:"103965",title:"Dr.",name:"Hamid",surname:"Yaghoubi",slug:"hamid-yaghoubi",fullName:"Hamid Yaghoubi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7524",title:"High-Speed Rail",subtitle:null,isOpenForSubmission:!1,hash:"0e248745ed8a460687701d02462cb874",slug:"high-speed-rail",bookSignature:"Hamid Yaghoubi",coverURL:"https://cdn.intechopen.com/books/images_new/7524.jpg",editedByType:"Edited by",editors:[{id:"103965",title:"Dr.",name:"Hamid",surname:"Yaghoubi",slug:"hamid-yaghoubi",fullName:"Hamid Yaghoubi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3631",title:"Smart Home Systems",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"smart-home-systems",bookSignature:"Mahmoud A. 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\r\n\tMechanical energy is the most common form of alternative energy that can be absorbed and turned into usable electricity. In comparison to electrostatic, electromagnetic, and triboelectric transductions, piezoelectric transduction is the most common mechanical energy harvesting mechanism due to its high electromechanical coupling factor and piezoelectric coefficient. As a result, the scientific community is very interested in piezoelectric energy harvesting. Micro and nanoscale materials and manufacturing processes have enabled the fabrication of piezoelectric generators with inherent advantages such as increased piezoelectric coefficient, electromechanical coupling factor, stretchability, flexibility, and integrate-ability for a variety of applications. Furthermore, with technological advancements in the electronics industry, tiny components with lower power consumption are becoming available on the market. As a result, many devices are expected to be powered by piezoelectric generators soon. This book aims to provide a complete overview of piezoelectric energy harvesting technology. The operating mechanics and operational modes of piezoelectric generators will be explored, as well as the piezoelectric energy conversion concepts. The latest research in inorganic, organic, composite, and bio-inspired natural piezoelectric materials will be discussed. The use of piezoelectric energy harvesting at the nano, micro, and mesoscale in a variety of fields will also be discussed, including aircraft applications, smart systems, microfluidics, biomedical devices, structures, transportation, wearable and implantable electronics, water applications, and tissue regeneration. We hope to examine the breakthroughs, limitations, and possible enhancements of piezoelectric energy harvesting technology materials and applications. In a brief, this book will cover a wide range of piezoelectric materials that can be used to provide clean power to wireless devices in a variety of applications.
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He regularly serves as a reviewer of scientific articles in RSC Advances, Royal Society Open Science, Materials Science and Engineering B, New Journal of Chemistry, Sensors and Actuators B: Chemical, Applied Surface Science, Journal of Alloys and Compounds, Materials Today Communications, Sensors, Aggregate, Advances in Polymer Technology, IEEE Electron Device Letters, and IEEE Transactions on Nanotechnology.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"182114",title:"D.Sc.",name:"Rafael",middleName:null,surname:"Vargas-Bernal",slug:"rafael-vargas-bernal",fullName:"Rafael Vargas-Bernal",profilePictureURL:"https://mts.intechopen.com/storage/users/182114/images/system/182114.jpeg",biography:"Rafael Vargas-Bernal received a bachelor's degree in Communications and Electronics Engineering from the University of Guanajuato in 1995, and the degrees of Master of Science and Doctorate in Sciences with Specialty in Electronics from the National Institute of Astrophysics, Optics and Electronics (INAOE) in 1997 and 2000, respectively. 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Some disasters build up slowly while others may happen suddenly and unexpectedly. Flood disasters can be classified among the quick and sudden disaster types, but are among few in this category that can be well predicted, anticipated and controlled to a great extent.
Floods, like other disasters, do not qualify to be labelled ‘disasters’ by the mere virtue of their happenstances. They do become disasters when they cause damage or adverse effects to human lives, livelihoods and/or properties. Floods are probably the widest spread among the various disaster events that occurs in most countries and causes the most deaths [1]. Floods, like other disasters, have the ability to cause widespread disturbances in communities, and alter the way of life of people in the affected areas.
The word flood originated from the old English word ‘flod’ akin to the German word ‘flut’ and the Dutch word ‘vloed’ seen as inflow and float of water [2, 3]. The Oxford Reference Dictionary (ORD) defines flood as an overflowing or influx of water beyond its normal confines. Floods usually happen when the volume of water within a water body, say, a river or a lake, exceeds its total carrying capacity and as a result, some of the water flow outside the normal perimeter of the water body. Floods occur in almost every part of the world with different intensities and effects. Some of the most notable floods that have occurred include the 1981, 1991 and 2002 floods along the Chiang Jiang (Yangtze) river in China, the Mozambican floods in 2000, the 1983 and 1993 floods on the Mississippi river [2].
In the summer of 2005, the remarkable flooding brought by Hurricane Katrina which caused more than $ 108 billion in damages, constitute the costliest natural disaster in U.S. history [4, 5]. Identified different types of floods namely riverine floods, localized and urban floods, normal flood (e.g. 1-year flood), medium flood (e.g. 5-year flood), severe floods, and catastrophic floods. It is indicated that floods can also be distinguished by their style of occurrence [2]. Flash floods occur when water quickly sweeps over an area which is difficult to deal with and it is not easy to predict the amount of rain expected within the spatial area over a short period of time [2].
Regional floods occur when rain falls over a large area for days or weeks causing river levels to rise quickly and fall slowly usually inundating large areas and causing widespread economic losses [6]. Flash floods are also referred to as upstream floods and regional floods, downstream floods [7].
There are varied effects of floods. The primary effects of flooding include physical damage to buildings and weakening of structures [2]. There are instances of loss of human lives and livestock, and the outbreak of disease epidemics. Other effects include instant losses of entire harvest as in the Mozambique flood in 2000 and northern Ghana floods of 2007. Whilst the effects of floods have come to be highly perceived in the negative, it is also true that floods are not entirely of damaging impact on human beings. Flooding can be beneficial such as making the soil more fertile and providing nutrients. Periodic flooding was essential to the development of some of the ancient civilizations especially those along the Tigris-Euphrates rivers, the Nile river, Indus river among others [2].
Floods happen when soil and vegetation cannot absorb water from downpours. Floods also occur when a river outbursts its banks and the water spills onto the floodplain. Natural processes such as hurricanes, weather systems and snowmelt can cause floods. Other floods following tsunamis and coastal surges have natural causes like earthquakes in the seabed and high tides attributed to the pull of the moon [2]. There are many human-induced causes of flooding.
Urbanization has also become a major cause of flooding in cities [8], such that, a river is more likely to flood when its drainage basin is in an urban area. Inadequate drainage in some urban areas is a major cause of flooding [3], while in others, it is the lack of proper management of the drainage systems. Unplanned urban living has been identified as a significant contributor to flooding events in many developing countries. In a study into causes of flooding in Asamankese in the Eastern region of Ghana by [9] for instance, a resident succinctly summarises the problems as below;
[T]he main problem in Old Zongo and Abaase areas is the gutters. The gutters are not enough to carry the water when it rains heavily, and secondly, they pour so much rubbish in the gutters, so some of the gutters are also full of rubbish. So, when it rains heavily, where will the water go, it must flood the area. ….the way we build in this area too is a problem. I even think government is not hard on people so we just build anyhow in the waterways. We in this area also experience floods but it is not serious like in Old Zongo areas, that is why we are always trying to tell people here not to build in the waterway, because of what is going on in Old Zongo and Abaase.
This summarises the major contributions of improper managed urbanisation to flooding, a phenomena which characterises many developing countries. In Ghana, for example, perhaps the most devastating flood in the history of the country occurred in its capital, Accra, on 3rd June, 2015 where 159 people lost their lives and several people rendered homeless [10]. NADMO [11], suggests that although Ghana is vulnerable to certain disasters, flooding has become the major disaster the country has suffered in recent years especially in its urban areas due to improper management of these spaces [12].
Figure 1 shows the nature of some gutters in the urban areas of Ghana. Figure 1 shows a partially completed drainage in the flood prone zone of Asamankese, in the Eastern region of Ghana. Most of the gutters in the community, like Figure 1, are left open and easily gets silted by inflow of sand and other waste materials. The situation of improper management of urban spaces is worse in the major Central Business Districts in many developing countries. Plastic wastes and other debris have been left to clog urban drainage which results in flood disaster when heavy rains are experienced.
Showing a partially completed drain in the Asamankese community, Eastern region of Ghana. Source: Author.
What flood events share in common, is their ability to cause widespread community disruption, displacement, economic loss, property damage, deaths, injury as well as profound emotional suffering. Infrastructure and property, agricultural endeavours as well as historical and cultural sites may also be affected in flood disasters.
According to the United Nations Regional Coordinator in Dakar (October 2007) the worst flooding in 30 years that battered West Africa from July 2007 caused more than 210 death and affected more than 785,000 people [12].
The aftermaths of flood disasters in Ghana are the large-scale destruction of infrastructure, displacement of people from their dwellings, the loss of human lives, outbreak of diseases and water-borne infections, chemical exposure due to toxic pollutants being released into flood waters, huge loss of investments among other things.
Africa, which is one of the poorest continents in the world (in terms of GDP growth and income) has seen an increase in flood disasters in recent times [9]. For instance, torrential rains and flooding affected 600,000 people in 16 West African nations in September 2009 [13]. Countries with most devastating impacts were Burkina Faso, Senegal, and Niger. Another instance include the 2007 floods that displaced more than a million people in Uganda, Ethiopia, Sudan, Burkina Faso, Togo, Mali, and Nigeria, which claimed over 500 lives, and the 2008 floods in Mozambique which killed seven people and displaced tens of thousands residence [14]. Heavy seasonal rainfall starting in December 2014 also caused flooding in southern Africa [15]. As of January 2015, 135,000 people were affected by flood hazard in Malawi, Mozambique, Madagascar and Zimbabwe [15].
The impact of flooding varies both spatially and temporally. It could also be direct or indirect. Rahman [16] indicated that the direct impacts of floods are closely related to the depth of inundation of floods water. The extent of a flood has a direct relationship for the recovery time of crops, pastures and the social and economic dislocation impact to populations. The impact of floods is considered far reaching with the aftermath effects such as flood-induced disease epidemics. Disease outbreak is common, especially in less developed countries. Malaria, Typhoid and Cholera outbreaks after floods in tropical countries are also common [17]. [9] further stated that physical damage to property is one of the major causes for tangible loss in floods. This includes the cost of damage to goods and possessions, loss of income or services in the floods aftermath and clean-up costs. Some impacts of floods, on the other hand, are intangible and are hard to place a monetary figure on. Intangible losses also include increased levels of physical, emotional and psychological health problems suffered by flood-affected people.
According to [15] the cumulative number of people affected by rains and floods in 2007 in Southern Africa was more than 194,103 persons. This included 60,995 in Malawi (Mostly damage to property and crops), 94,760 people in Mozambique (all were evacuated into resettlement camps); more than 16,680 in Zambia (1890 persons had temporary accommodation, the rest were taken in by host families); and 15,168 in Zimbabwe. An estimated additional 4000 people had been affected in Lesotho and another 2500 persons in Swaziland.
Extreme events affect both the formal and informal economies, making it difficult to assess impacts which include direct and indirect ones. Depending on how well they are constructed and the severity of the event, buildings may be partially or totally destroyed by flooding. A look at Figure 2 will explain the partial damage that often happens to buildings as a result of flooding.
Depicting the impact of flood events o residents’ household in Asamankese, Eastern region of Ghana. Source: Author.
Flood destructions also hit roads and cause delays to infrastructure development initiatives and political processes [18, 19] observed that the economic impact of natural disasters shows a marked upward trend over the last decades. The hazards tend to hit communities in developing and least developed countries more. Flood disasters have led to the loss of human life, destruction of social and economic infrastructure and degradation of already fragile ecosystems [20]. It follows therefore that social impacts include changes in people’s ways of life, their culture, community, political systems, environment, health and wellbeing, their personal and property rights and their fears and aspirations.
Rahman [21], established that social impacts of floods cause significant problems for the long term functioning of specific types of households and businesses in affected communities. The type of construction influenced the extent of flood damaged (e.g. thatched homes versus concrete high rise buildings will experience different degrees of impact). It follows that vulnerability is a key element in assessing the impact of floods. Different population segments are exposed to varied relative risks because of their socioeconomic conditions of vulnerability. Because of this, disaster reduction has become increasingly associated with practices that define efforts to achieve sustainable development. The links between flood disaster and economic systems, have become another pillar of consideration for sustainable development. Floods, however, cannot be totally prevented but their devastating impacts can surely be significantly minimized if advance warning of the event is available.
Disaster risk awareness is the extent of common knowledge of a person or group of persons about disaster risks, the factors that lead to disasters and the actions that can be taken individually or collectively to reduce vulnerabilities to hazards. It also includes the need to build and increase the knowledge and understanding of the many issues about disaster risk reduction, to build the capacity of the people who learn and teach others about the disaster [9]. Changes in patterns of human behaviour and decision-making at all levels of government and society could, therefore, lead to a substantial reduction in disaster risk [22]. In this respect, recent experience has shown that public awareness of natural hazards and disaster risk reduction education constitutes a foundation and pre-requisite for effective catastrophic risk management strategies at country and regional levels. More importantly, by influencing human actions and perceptions through societal behaviour and behavioural adaptation, information and education can increase flood risk awareness and play a more effective role in reducing the costs of catastrophes associated with natural perils [22].
To proffer an understanding on the flood risk awareness of residents in the Asamankese Municipality of Ghana, [9] surveyed some residents and sought to know how likely the area was susceptible to flood through the major rainy season from March to July. Figure 3, which summarises the respondents result, pointed out that an overwhelming proportion of respondents (70.0% and 77.5% within the flood prone and Non-flood prone zones respectfully) indicated that flooding has become a regular phenomenon, and the community was likely to be flooded every time it rained.
Showing respondents awareness to flood disaster risk at Asamankese, Eastern region, Ghana. Source: Author.
Awareness is a very crucial element for a society to effectively adapt to a flood risk. As stated by [23] awareness is diminished when the provision of an appropriate information is minimal or when memories of past experiences or events are diminished. Awareness can generally be uplifted through efforts that are centred on local issues, contain the simple solution to reduce the flood risk and are repeated on a regular basis [24].
Scholars like [25] posit that worry is an important risk characteristic that serves as a normative value for awareness. Society can be aware of a flood risk, however, if it is not afraid of the risk, it will not take any action to prepare for it. A higher level of worry is more likely to result in a higher level of awareness and preparedness. There is a positive correlation between these two variables. This was reinforced by [26] with their assertion that most people become aware and prepared for disasters based on their previous personal experiences with flood disasters. Early warning information can, therefore, allow the disaster managers to be pre-informed and take steps which may significantly reduce the loss of life and damage to property.
The argument now is that adequate preparation can make it possible to significantly reduce the impacts of flood disasters through a good understanding of preventive action as well as knowledge of some life-saving techniques during disasters [27]. Nowhere has the issue of floods become a developmental issue than in poor and developing countries where systemic problems and institutional constraints have increased vulnerability (social, economic and physical) to flood risk and thus, reducing resilience to flood disasters.
Disaster preparedness is defined as the state of taking measures to reduce to the minimum level possible, the loss of human lives and other damages from flood disasters through prompt and efficient actions of response and rehabilitation. That is, preparedness is to put in place the necessary measures for effective and timely response to an event. The objectives of preparedness are to ensure that appropriate mechanisms and resources are in place to assist those afflicted by the disaster and enable them to help themselves [9].
Flood disaster preparedness consists of a wide range of activities and protective measures that might be instigated from the physically or procedurally. Preparedness is very important in the disaster management process, and includes the knowledge, capacities, activities and measures carried out in advance by governments, professional response and recovery organizations, communities and individuals to effectively anticipate, respond to and recover from the impacts of imminent or current disaster situations or conditions.
The [26], conceives preparedness as a medium-term plan that involves the development and the implementation of disaster management plans. It involves the development and implementation of early warning systems, resource inventory and stockpiling of resources, coordinating of agencies and ensuring evacuation plans work. Preparedness is seen as tools for ensuring the effective coordination and enhancement of capacities to prevent, to protect, to respond to, recover from and mitigate the devastating effects of natural and man- made disasters [9].
Apart from personal experiences informing residents’ awareness and preparedness towards flood disasters, external factors such as occupation, level of education of an individual, radio programs and community meetings serve as conduits for disseminating flood information. Hence, these factors may act to increase awareness and preparedness levels to flood disasters.
Studies show that residents’ awareness of flood disaster is usually high. For instance, in [9]’s study of residents’ level of flood disaster awareness in Asamankese, in Ghana, showed that more than 65 percent of residents in both the Flood prone and Non-flood prone settlements ranked themselves to be at a high risk of flood disasters. Furthermore, the study showed that, the awareness of human factors that exacerbate their risk to flood was also high. However, preparedness of residents’ in most cases were poor, and in Asamankese, like in other developing countries, victims of flood had to usually depend on extended social networks, and government institutions for support to regain their livelihoods after being hit by flood events, a situation which results from their ill preparedness to flood disasters especially financially.
Flood Disaster Management Strategies refer to a bundle of processes and activities that are aimed at reducing the overall impacts of floods on societies. Flood management needs to be considered within the overall national development planning strategy of every country and must involve strategic institutional arrangements and collaborations for a sustainable flood management.
The management of floods as problems in isolation almost necessarily results in a piecemeal, localized approach (World Meteorological Organization [28]). The flood disaster management process should also be coordinated with efforts made in closely related fields. For example, the disaster mitigation process should consider human health impacts during flooding (e.g. cholera, malaria), thereby more effectively address a health issue that arises during and after flooding [28].
The management of floods takes several approaches ranging from traditional approaches to integrated approaches. The traditional management response to a severe flood was typically an ad- hoc reaction, the quick implementation of a project that considered both the problem and its solution to be distinct and self-evident. Traditional approaches usually gives no thought to the consequences for upstream and downstream flood risks [28]. Thus, flood management practices have largely focused on reducing flooding and reducing the susceptibility to flood damage. Traditional flood management has employed structural and non-structural interventions, as well as physical and institutional interventions. These interventions have occurred before, during and after flooding, and have often overlapped.
There has been a paradigm shift in flood management. Traditionally, controlling floods has always been the main focus of flood management, with the emphasis on draining flood water as quickly as possible, or storing it temporarily, and separating the river from the population through structural measures such as dams and levees [28].
The concept of integrated flood management has led to a paradigm shift: absolute protection from floods is a myth, and focus should aim at maximizing net benefits from the use of flood plains, rather than trying to fully control floods [28].
A proactive approach towards the management of floods over a traditionally reactive approach is rapidly gaining recognition among flood managers. The proactive approach does not treat floods only as an emergency or an engineering problem, but as an issue with social, economic, environmental, legal and institutional aspects. The proactive approach is not limited to a post-event reaction but includes preparedness (including flood risk awareness) and response measures to flood management at different stakeholders’ levels [28].
Recent calls in flood management are geared at taking a transboundary approach since floods do not respect borders; neither national nor regional or institutional [29]. The great advantages of transboundary cooperation are that it broadens the knowledge/information base, enlarges the set of available strategies and enables better and more cost-effective solution. Furthermore, widening the geographical area considered by basin planning enables measures to be located where they create the optimum effect [29].
The term ‘early warning’ is used in many fields to describe the provision of information on an emerging perilous circumstance where that information can enable action in advance to reduce the risks involved. The early warning system comprise the set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations that are threatened by hazards to take necessary preparedness measures and act appropriately in sufficient time to reduce the possibility of harms or losses [30].
Early warning systems exist for natural geophysical and biological hazards, complex socio-political emergencies, industrial hazards, personal health risks and many other related hazards. Studies have demonstrated that disaster prevention can pay high dividends and found that for every Euro invested in risk management, broadly 2 to 4 Euros are returned in terms of avoided or reduced impacts on life, property, the economy and the environment [31]. Early warning systems can be set up to avoid or reduce the impact of flood hazards and other disasters such as, landslides, storms and forest fires and volcanic eruptions. The significance of an effective early warning system lies in the recognition of its benefits by the members of the general public.
Early warning is a major element of flood disaster risk reduction. It saves life and reduces economic and material losses from flood disasters. To be effective, community-based early warning systems need the active involvement of the community people, a strong public education on and awareness of risks, an effective communication system ensuring a constant state of preparedness [31]. Early warning systems contribute with other Disaster Risk Reduction (DRR) interventions to protect and support sustainable economic development and early detection of undesirable situations. The society benefits from early warning systems if they are in place. Many governments have failed to take early warning into account while formulating their development and disaster risk reduction policies. Subsequently, it results in heavy losses to human lives and economic entities when disasters strike [31].
A people-centered early warning system necessarily comprises four key elements: (I) knowledge of the risks; (II) monitoring, analysis, and forecasting of the hazards; (III) communication or dissemination of alerts and warnings, and (IV) local capacities to respond to the warnings received. The expression “end-to-end warning system” emphasizes that early warning systems need to span all steps from hazard detection to community response. It is essential to link downstream communities and upstream communities for the effective operation of an early warning system [31]. There are several instances where early warning systems have helped to mitigate the impact of disasters. As an example, the Bangladesh cyclone preparedness program has successfully warned, evacuated and sheltered millions of people from cyclones since its inception in the early 1970s by the International Federation, the Bangladesh Red Crescent Society and the government of Bangladesh. In the Caribbean, during 2004’s hurricane season, most countries successfully alerted their populations to approaching storms and saved many lives as a result. The key to their success was putting people, not just technology, at the centre of their warning systems. As a result of early warning systems, there were no deaths reported in La Independencia, Guatemala during the hurricane season in October 2005 [26].
The importance of early warning has been underlined in various UN General Assembly resolutions as a critical element of disaster reduction. Early warning received very high attention after the 26 December 2004 tsunami, when it became clear that a tsunami warning system and associated public education could have saved thousands of lives. The UN Secretary-General in his report;
Flood Disaster Mitigation measures tend to be potentially more efficient long term sustainable solutions to water-related problems and should be enhanced, in particular, to reduce the vulnerability of human beings and goods exposed to flood risk. Flood forecasting and warning is a prerequisite for successful mitigation of flood damage. In the field of environmental engineering, flood mitigation involves the managing and control of flood water movement, such as redirecting flood run-off through the use of floodwalls and flood gates, rather than trying to prevent floods altogether. It also involves the management of people, through measures such as evacuation and dry/wet proofing properties for example. The mitigation of flooding can be done on an individual, community and at city authority or national levels.
Flood disaster adaptation refers to actual adjustments made that are geared towards mitigating the severity of flood disasters. Flood disaster adaptation strategies vary from before flood adaptation, during flood adaptation, to post flood adaptation strategies. It also ranges from individual, community, to citywide adaptation strategies. Discussions on flood adaptation strategies pointed out that embankments, for instance, either concrete or sandy may be constructed to prevent water from entering residential houses [33]. Adaptation options that would be effective for flood disaster in developing nations include Environmental policy reforms, changes in urban and housing design, removal of laws that can inadvertently increase flood vulnerability [34]. Capacity building is also required to integrate climate change and its impact on urban development planning, engaging local communities, raising public awareness and education on climate change and enabling wider representation at stakeholder meetings. Planting of vegetative cover to reduce runoff speed, terracing hillsides to slow flow down hills as well as control of man-made channels to divert flood water among others, serve as adaptation strategies. Generally, adaptation strategies adopted in flood disasters range from structural to non-structural [35].
Being able to count on institutionalized capacities to mobilize and coordinate resources when and where they are needed is crucial in all phases of the disaster cycle, sometimes with very little room for delay or errors of judgment. Coordination among agencies and stakeholder groups is important for flood mitigation, in particular, the design and execution of programmes and policies to help address underlying causes of extreme vulnerability [36]. Monitoring of activities is necessary because there is often the need to link responsibilities and budgets for programmes over time. The performance of institutions and organisations responsible for disaster management should be monitored and evaluated on a regular bases. The relevance of monitoring and evaluation as a means of reducing flood disaster events cannot be overemphasised.
The capacity to monitor and evaluate flood prevention, mitigation, relief and recovery operations and institutional arrangements would create opportunities for learning and improve the accountability of authorities [36]. Monitoring is a key element in pre-flooding, flooding and post flooding stages of flood disaster management. Evaluation goes hand in hand with monitoring to assess the impact of flooding and the effects of key interventions engaged in mitigating the impact of flooding on people, infrastructure, and the environment in general. In a study on the environmental aspects of integrated flood management, [28], noted that adaptive management requires continuous monitoring of the state of the environment and evaluation at regular intervals.
The importance of monitoring has been recognized from various perspectives [28] Pre-plan monitoring of various natural processes provides the basic input for assessment of resource, risks and development options. Monitoring at a development planning level is based on actions taken in line with selected plan and factors of environmental impacts indicated in environmental assessment at the strategic level [28]. In the context of awareness of flood hazards, monitoring encapsulates awareness of causes and how these causes change over time, knowledge of interventions and how these interventions are shaping the frequency and nature of flood events in an area. Monitoring is essential in flood management from a first-hand point of providing timely and efficient early warning information. Immediate and post-implementation monitoring is important in order to assess whether the flood management measure has succeeded [28].
Mainstreaming of disaster risk reduction into development planning, policy, and implementation should be at the heart of every sustainable Development Planning agenda. Disasters, such as floods, have an enormous impact on development. There is, therefore, the need for mainstreaming disaster planning into development planning. The importance of mainstreaming is also recognized by the Hyogo Framework for Action (HFA) adopted at the World Conference for Disaster Risk Reduction (WCDRR), where integration of disaster risk reduction into the development programmes is a priority [5]. There has been increasing recognition by both governments and donors for the need to mainstream disaster risk reduction into development planning [37]. Mainstreaming disaster planning into development planning considers risks emanating from natural hazards in medium-term strategic development frameworks, in legislations and institutional structures, in sectoral strategies and policies, in budgetary processes, in the design and implementation of individual projects and in monitoring and evaluating all of the above [37].
The concept of sustainable development is firmly rooted in all flood management. Sustainable flood management involves: ensuring quality of life by reducing flood damages but being prepared for floods, mitigating the impact of risk management measures on ecological systems at a variety of spatial and temporal scales, the wise use of resources in providing, maintaining and operating infrastructure and risk management measures, maintaining appropriate economic activity [38]. Sustainable flood management as a concept is not new, its methods have been practiced on many continents for years [39]. With increasing scrutiny of traditional engineering solutions, there is a growing realisation throughout the world that there is a huge and urgent need for pro-active and sustainable flood management solutions.
The notion of sustainability in the context of flood management is still rather ambiguous but generally embraces economic, environmental and social objectives. Sustainable flood management therefore refers to the provision of possible social and economic resilience against flooding, by protecting and working with the environment, in a way which is fair and affordable both now and in the future [40]. In practice a sustainable approach should integrate a range of flood management requirements using best practices and involving the economics of a scheme, good planning, understanding flood generation processes, protecting natural environments and working with communities [39]. Sustainable flood management is, therefore, an integrated set of procedures linked into a physical catchment.
Advanced technologies have been developed and integrated into higher institutional level decision support systems to aid the prediction, monitoring and management of flood disasters in some countries. These advanced flood decision support systems’ architecture include technologies such as Geographic Information Systems, remote sensing and photogrammetry, and hydrologic models.
The Flood Decision Support System (FDSS) refers to interactive computing environment designed for specific contexts which include interlinked models/analytical tools, databases, graphical user interfaces and other systems. The FDSSs according to [41] have the potential to improve flood disaster assessment and mitigation through improved data collection and rapid dissemination of flood information to affected areas. For an effective FDSS on the technology aspect of disaster management, analysts have to ensure effective interoperability of the technologies. This will ensure that, all aspects of the technology that singularly may be responsible for data capture, storage, manipulation, analysis, retrieval or display of information, work in a smooth interwoven network and relay information to other parts of the system without technical hindrances to ensure the overall goal is achieved.
There are three main components to the Flood Disaster Support System. These include the Database component, the Modelling component, and the Display component also known as the Graphical User Interface (GUI) component. The Database component of the FDSS comprises the data used in the modelling functions. This component uses to tools to capture and store flood related data. Some data stored include historical rainfall data, geological data, soil and ecological data, population data, boundary and administrative data. Tools used in data capture for the Database varies depending on the data to be captured. For example, Remote sensing techniques are used to capture satellite data on flood zones, flood buffer zone monitoring. Sensors are also deployed to monitor flow, volume and carrying capacities of rivers while rain gauges capture precipitation volumes. These data may be complemented with census data on population and livelihoods of residents. All these various data are kept in the Database component of the DSS.
The second component of the FDSS are functions of analytics and modelling. Various analysis are carried out and the data in the database taking through several processes of manipulation. These processes of data manipulation and analysis differ in approach and are tailored to meet various goals in the decision making process. Prominent among the tools used at this stage is Geographic Information Systems (GIS) tools. Regarding flood modelling, advanced tools available to flood managers include advanced technological tools in soft computing, for instance, evolutionary computing, as well as probabilistic predictions techniques of inundation recurrence intervals [41]. These tools afford flood managers varieties of techniques that can be applied in simulation, modelling, analysis and management of flood.
The User Interface component of the FDSS provides flood decision makers an interactive graphical interface, enabling users to query the data stored in the system. It again enable users to display and visualise the models and reports from the manipulations of the data. This component of the advanced FDSS enables users to prepare and appreciate maps and animations of the hydrologic phenomena being studied.
Advances in remote sensing tools and techniques over the past few years have provided disaster managers, especially flood disaster managers with powerful tools in the acquisition of flood sense data, in forecasting and monitoring of flood occurrences and in the management of watersheds, rivers and wetland areas.
Remote sensing refers to the Science of obtaining information about objects, areas or phenomena from a distance [42]. Typically, these information are collected through sensors that are planted on aircrafts or satellites. In flood disaster management, remote sensing can be applied to monitor and map events such as changes in river volume, changes in coastline, map wetlands and flood prone zones and boundaries of inundation.
A Geographic Information System(s) (GISs) refers to a framework for gathering, managing and analysing location-based data. This framework is used to analyse and organize several distinct layers of location-based information into concise visualizations through maps and 3D scenes. Ultimately, GISs present powerful capabilities that proffer deeper insights into data, which may include revelation of patterns and relationships for smarter decision making [43].
Reliable flood maps are therefore produced using GIS techniques and remotely sensed data to manage floods. GIS tools aid in the preparations to Digital Elevation Models (DEMs) for high level hydrological modelling using sensors such as The Light Detecting and Ranging (LiDAR) sensors.
With the help of data interpretation techniques of GIS, remotely-sensed imageries are interpreted to create suitable flood risk mitigation frameworks and FDSSs. Although flood disasters have increased in scale and frequency in recent years, there has been a commensurate improvement in flood data capturing and analyses techniques, that when applied in time, can significantly mitigate the risks and impacts of floods. As summarised in Table 1, GIS and RS are of great importance in the pre and post disaster management processes.
Flood Prevention | Capturing imageries for hazards and risks assessment. Preparation of flood prone maps. Management of large volume of flood sense data. |
Flood preparedness | As tools for planning evacuation routes. Designing centers for emergency operations. Integrating and Simulating live satellite data with other dataset to inform early warning systems. |
Flood relief | Planning and execution of search and rescue operations. Planning distribution of relief items to flood victims |
Flood rehabilitation | Flood impact assessment. Rehabilitation planning. |
Showing GIS and Remote Sensing Application in flood disaster management. Source: Authors’ Construct with reference to [44].
The Internet of Things (IoT) refers to a network of devices connected over the internet to sense, track and respond to issues. Patel and Patel [45] defines the IoT as “
The network of physical objects are able collect data on a regular bases and in a structured form, perform high level analysis and predict changes, as well as initiate actions based on results from the analyses. IoT is hence a powerful technological tool that can provide a wealth of high level intelligence which is needed in planning and management.
There are three levels of IoT. The first is people to people interconnectivity, the second is people to machine interconnectivity and the third being machine to machine or things to things interconnectivity [45]. In all interconnectivity of things and people, the internet remains the main driver. This interconnectivity of Things, enables the swift transmission of meteorological, hydrological and geological data pertaining to flood events.
In flood disaster management, providing a quick feedback on the occurrence of floods can be a great step in preventing and mitigating flood disasters and their impact on livelihoods in society. Deploying IoT in flood management puts disaster managers at a position to create enhanced early warning systems that do not only measure the water levels and the speed of inundation, but early warning systems that could also send alerts to residents and flood managers through mobile phones and other personal electronic devices, and additionally, prescribe the best prevention and mitigation strategies based on data such as direction of runoff, speed of rise of water levels and the time at the disposal of residents to take necessary action.
Big Data on the other hand, refers to “
Big data typically defines data that exceeds the storage, processing and computing capacity of conventional database [46]. Hence Big Data analytics typically involves automated software that assist in the collection, organisation and analysis of the data being generated to discover trends, correlations and other useful results to prompt necessary action.
Through Big data process automation, precipitation data, soil moisture data, temperature data, water content data of water bodies, data on evapotranspiration, ground water data, etc., are collected and processed in real-time without human supervision to make predictions and early warnings about flood disasters’ occurrence [47].
Flood disasters have had very devastating impacts on societies and have destroyed livelihoods and investments of staggering monetary value and importance to development. However, adequate involvement of technology are leading to the creation of people-centered early warning systems that enhances residents’ awareness and preparedness to flood events to significantly reduce the adverse impacts of these disasters on people. This chapter discussed various aspects of flood disaster management including early warning systems, flood mitigation and adaptation strategies, the relevance of monitoring, evaluation and mainstreaming flood disaster management into national level development planning. The chapter again discussed and encourage the integration of advanced technological tools into the frontier of flood disaster management, as these tools have the capacity to capture, analyse and disseminate real-time flood data to all stakeholders to safeguard lives and precious investments.
Agriculture, being man’s most fundamental profession, has benefitted immensely from technological advancements ranging from shifting cultivation to high precision farming. With the advent of civilization, man learned about additional crops and began to produce a variety of crops. As the human population grew and civilization progressed, people began to dwell in one location and farm the same land year after year. Now that agriculture has evolved into a profession, it is known as commercial agriculture, with precision agriculture and sustainable agriculture as key components.
The world’s population is rapidly increasing these days. To meet the rising demand for food, the agricultural community must produce more and more. Because it is difficult to bring additional land under cultivation (extensive farming) in the current scenario, when land is a limited issue, the agricultural community should take on the task of producing more and more food with the land that is available (intensive farming). On the contrary, growing worldwide concern for human health and the environment opposes the use of higher levels of pesticides and fertilizers, as well as genetically modified plants. The latter, on the other hand, are present technologies that have the potential to improve food production.
Crop needs and soil/land conditions influence adaptability. Suitability is determined by matching the land features to the crop needs. Suitability is a measure of whether a land unit’s features meet the needs of a certain type of land use (FAO). Aside from land and soil qualities, additional driving elements that might impact crop choices include socioeconomic, market, and infrastructural factors.
The FAO Land Evaluation Framework is based on previous land capabilities methodologies. In this case, the overall land appropriateness of a land area for a particular land use is assessed using a series of more or less independent land attributes, each of which may limit the land-use potential. These assessments are frequently used to classify map units in natural resource inventories. A soil survey’s legend categories are divided into suitability subclasses based on the quantity and severity of land use restrictions.
In the FAO framework, there are two sorts of categories based on the scale of measurement of appropriateness.
Qualitative: in reconnaissance investigations, the classes are rated based on the physical production potential of the land. It is employed to assess environmental, social, and economic factors.
Quantitative: the classes are specified in numerical terms that allow for comparison of the objectives. There are a lot of economic parameters employed here.
By introducing quantification of land suitability indicators over a whole area, quantified land evaluation [1] revolutionized land suitability evaluation. The area is divided into small grid cells, and cell-based modeling has started. The indicators, on the other hand, must be quantitative. Geographical information systems and geostatistical approaches are commonly used in such land suitability analyses.
The FAO Framework identifies four categories of increasing details, as shown in Table 1.
S. No. | Categories | Explanation |
---|---|---|
1 | Land Suitability Orders | Reflecting kinds of suitability |
2 | Land Suitability Classes | Reflecting degrees of suitability within Orders |
3 | Land Suitability Subclasses | Reflecting kinds of limitation, or main kinds of improvement measures required, within Classes |
4 | Land Suitability Units | Reflecting minor differences in required management |
FAO structure of land suitability classification.
Land appropriateness is a factor in determining a land use’s long-term viability. The sustainability of a land use is defined by its suitability and vulnerability. Maximum appropriateness and minimum vulnerability should be the goals of sustainable land use (Figure 1) [2].
Land use sustainability (after [
According to Rossiter [3], land is distinctive in every location, and this uniqueness has an impact on land usage. He also mentions how land evaluation might help with agricultural support services.
The multi-criteria land suitability was evaluated in a non-spatial manner, assuming spatial homogeneity across the study region. However, in circumstances like land suitability studies, when decisions are made based on factors that change over space, this is impossible [4]. Non-spatial traditional MCDM techniques average or total the effects that are judged appropriate for the entire area under consideration to address the spatial decision [5]. Jankowski [6] suggests that making, MCE, and GIS can all be combined. For many crops, MCE appears to be applicable in GIS-based land suitability analyses [7].
Ranking and rating are two widely used MCE approaches in land suitability evaluations. These methods lack a theoretical underpinning in determining the weights. The weights are assigned quite haphazardly in these procedures. They do not take into account comparisons between criteria and classifications. Furthermore, the results of such an investigation are grouped together using a simple Boolean overlay or weighted aggregation.
Since its beginnings, several researchers have examined the Analytic Hierarchy Process (AHP) [8]. The Analytic Hierarchy Process (AHP) is a method for making multi-criteria decisions (MCDM). The earliest reference we have identified is from 1972 [9]. The method was then discussed in detail in a paper published in the Journal of Mathematical Psychology [10]. The vast majority of applications continue to use AHP in the manner specified in this first article, oblivious to subsequent developments. This study draws out the significant trends in methodological advancements and future research in this vital topic.
AHP has been widely used since its introduction, for example, in flexible manufacturing system [11], Machine selection [12], industrial R&D project selection and resource allocation [13], Delphi method [14], Computer-aided machine-tool selection [15], evaluating machine tool alternatives [16], Integrating fuzzy theory and hierarchy concepts to evaluate software quality [17], product design in concurrent engineering [18]. Issue resolution for conceptual design using AHP [19]. Selection of appropriate schedule delay analysis method [20].
Northern region India is a city in India’s Punjab state. On July 27, 2011, Northern region India was formally designated as a district of Punjab state (Previously it was a Tehsil of Gurdaspur district, Punjab). Northern region India district is located in Punjab’s northernmost region (Figure 2).
Location of the study area.
It is where the three northern states of Punjab, Himachal Pradesh, and Jammu and Kashmir come together. Northern region India serves as a transportation hub for the three northern states due to its strategic location. It is the last city in Punjab on the national highway that connects Jammu and Kashmir to the rest of the country. Northern region India is also a major educational center for the nearby states of Jammu and Kashmir and Himachal Pradesh. It is located in the Jalandhar division, between the Ravi and the Beas rivers.
Northern region India district is located between 32°23′31″ and 32°23′52″ north latitudes and 75°39′55″ to 75°56′12″ east longitudes and covers an area of 27,123 ha. On a 1:50 K scale, the Survey of India 43 P/11, 43 P/14, and 43 P/15 top sheets cover the area.
See Table 2.
S. No | Data set | Spatial resolution/scale | Source |
---|---|---|---|
1 | Sentinel 2 | 10 m | Copernicus |
2 | SRTM Dem | 30 m | USGS Earth Explorer |
3 | Land use and land cover data | 100 m | NASA Earth Data ORNL DAAC |
4 | Soil map | 1:2000000 | European soil data center (ESDAC) |
5 | Geology | 1:2000000 | Bhukosh |
6 | Geomorphology | 1:250000 | Bhukosh |
Data set and data source.
The shuttle radar topography mission (SRTM) elevation data (30 m resolution) obtained from USGS explorer (https://earthexplorer.usgs.gov/) was used to create a digital elevation model (DEM) of the study area. Using the DEM data slope, aspect, drainage density, Elevation thematic layers were built using ArcGIS 10.5. The land use and land cover data is downloaded by Nasa earth data ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1336). Soil map was obtained from European soil data center (ESDAC) which was published by National atlas and thematic map organization, Department of science and technology (https://esdac.jrc.ec.europa.eu/content/national-atlas-india-northern-india-plate-199-soil-regions).Geology and Geomorphology data of Northern region India is downloaded from Bhukosh (http://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx). Sentinel2 data of the study area is downloaded from Copernicus(https://scihub.copernicus.eu/). Using the Sentinel 2 data NDWI was built in ArcGIS 10.5.These resulted thematic maps: Slope, LULC, NDWI and Drainage density were integrated in ArcGIS 10.5 and finally soil suitability map was obtained (Figures 3–11; Tables 3–11).
Slope map.
Elevation map.
Aspect map.
Drainage map.
Land use and land cover.
Moisture index map.
Soil map.
Geology map.
Geomorphology landform map.
Slope angle (°) | Area (ha) |
---|---|
0–3 | 66049.4 |
3–6 | 13244.1 |
6–12 | 8884 |
12–18 | 6252.5 |
18–36 | 5234.1 |
36–58 | 144.8 |
Slope angle and its area coverage.
Elevation (m) | Area (ha) |
---|---|
224–300 | 46573.82 |
300–400 | 21522.16 |
400–550 | 16241.04 |
550–700 | 13350.39 |
700–960 | 2098.89 |
Elevation and its area coverage.
Aspect | Area (ha) |
---|---|
East, West | 25205.63 |
North | 10275.67 |
Northeast, Northwest | 21756.82 |
South, Southwest, Southeast | 42581.89 |
Aspect and its area coverage.
Drainage density | Area (ha) |
---|---|
High | 24970.4 |
Medium | 37780.1 |
Low | 35291.3 |
Drainage density and its area coverage.
Class | Area (ha) |
---|---|
Water bodies | 8967.44 |
Evergreen broad leaf forest | 595.94 |
Crop land | 57921.61 |
Built up area | 6828.81 |
Deciduous broadleaf forest | 19649.35 |
Shrub land | 1749.07 |
Permanent wetland | 210.61 |
Wasteland | 2236.91 |
Mixed forest | 623.53 |
LULC and its area coverage.
Moisture index | Area (ha) |
---|---|
Good Soil Moisture | 13341.96 |
Medium Soil Moisture | 37086.93 |
Less Soil Moisture | 28455.66 |
Very Less and Dry Soil Moisture | 16474.77 |
Water Bodies | 4453.2 |
Moisture index and its area coverage.
Soil type | Area (ha) |
---|---|
Alfisol (alluvial soil) | 25614.38 |
Entisol (bhabar soil) | 46344.83 |
Ultisol (brown, red clay soil) | 27851.02 |
Soil and its area coverage.
Geology type | Area (ha) |
---|---|
Miocene | 8693.8 |
Miocene - Pliocene | 6611.5 |
Pliocene - Pleiostocene | 3241.69 |
Geology and its area coverage.
Geomorphology landform | Area (ha) |
---|---|
Active Flood plain | 6612 |
Older Flood plain | 12,852 |
Low and moderated Dissected Hills and Valleys | 11,488 |
Water bodies | 5848 |
Older Alluvial Plain | 34,920 |
Highly Dissected Hills and Valleys | 12,364 |
Channel Island / Bar | 572 |
Younger Alluvial plain | 9736 |
Geomorphology landform and its area coverage.
Because all the selected criteria are in different units, they must be converted to the same units in order to use the Weighted Overlay Method, which necessitates the use of a standardized value. Standardization techniques transform measurements into uniform units, and the resulting score loses its dimension as well as the unit of measurement for every criterion [21]. All the criteria maps’ vector layers were transformed to raster layers. After that, all raster layers were categorized and utilized as input data for the weighted overlay method, which resulted in the creation of the agricultural suitability map. The sub-criteria were ranked on a scale of one to ten, with one being the least significant and ten being the most significant.
One of the most important multicriteria decision-making strategies is the analytical hierarchy process. The procedure is used for a set of criteria or sub-criteria to create a hierarchical structure by assigning weight to each criterion [22].
The analytic hierarchy process provides a structural foundation for quantifying the strong comparison of design criteria and elements in a paired technique, reducing the decision-making process’s complexity [10, 23]. The weight values are determined using a pairwise comparison technique based on the relative significance of the criterion, two at a time [23]. By picking the eigenvalue corresponding to the highest eigenvector of the completed matrix and normalizing the total of the factors to unity, the analytic hierarchy method derives the weights for each individual criterion using the pairwise comparison matrix [4, 24, 25].
The pairwise comparison matrix was generated using the analytic hierarchy procedure described above, using a scale of 1–9, where 9 represents important relevance and 1 indicates equal relevance of the in between criterion of the matrix presented in (Table 12) [4, 24, 25].
Relative importance | Definition | Explanation |
---|---|---|
1 | Equal importance | Two criteria enrich equally to the objective criteria |
3 | Low importance of one over another | Judgments and experience slightly favor one criteria over another |
5 | Strong or essential importance | Judgments and experience strongly favor |
7 | Established importance | A criteria is strongly favored and its dominance established in practice |
9 | Absolute or high importance | The evidence favoring one criteria over another is of the highest probable order of affirmation |
2,4,6,8 | Intermediate values between the two adjacent importance or judgments | When adjustment is needed |
The fundamental scale for pairwise comparison matrix [25].
Reciprocals if criteria
The reciprocity criteria are mostly used in the comparison matrix, which is mathematically stated as n (
The CR is represented by Eq. (1), where CI stands for consistency index and RI stands for random index.
The consistency relationship aids in the determination of possible events and measures the decision maker’s/judgments’ logical inconsistencies [28, 29, 30]. It denotes the probability that the matrix judgments were produced at random [10, 31]. The Consistency Index and Random Index are the most important factors in determining the CR.
Equation (2) represents the Consistency Index (CI), in which k max is the principle or highest eigenvector of the computed matrix and n is the matrix order.
The Random Index (RI) is the mean value of the consistency index based on the computed matrix order as demonstrated by Saaty [10] (Table 13). If the CR value is [0.10], the weight values in the matrix show irregularities, and the approach (AHP) may not produce relevant results [25]. The calculated CR in this investigation was 0.0669, which is within acceptable limits, and the computed weight values are accurate. The obtained weight values are then transformed to percentages in GIS for weighted overlay analysis (WOA), as shown in Tables 14 and 15 (Figure 12).
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.46 | 1.49 |
Random inconsistency indices (RI) for
Criteria | Slope | Elevation | LULC | Soil moisture | Soil | Geomorphology | Drainage | Geology | Aspect |
---|---|---|---|---|---|---|---|---|---|
Slope | 1 | 2 | 2 | 3 | 4 | 6 | 7 | 8 | 9 |
Elevation | 1/2 | 1 | 2 | 3 | 4 | 5 | 7 | 7 | 8 |
LULC | 1/2 | 1/2 | 1 | 4 | 5 | 4 | 6 | 7 | 8 |
Soil moisture | 1/3 | 1/3 | 1/4 | 1 | 3 | 4 | 5 | 6 | 7 |
Soil | 1/4 | 1/4 | 1/5 | 1/3 | 1 | 3 | 4 | 5 | 6 |
Geomorphology | 1/6 | 1/5 | 1/4 | 1/4 | 1/3 | 1 | 3 | 4 | 4 |
Drainage | 1/7 | 1/7 | 1/6 | 1/5 | 1/4 | 1/3 | 1 | 2 | 3 |
Geology | 1/8 | 1/7 | 1/7 | 1/6 | 1/5 | 1/4 | 1/2 | 1 | 3 |
Aspect | 1/9 | 1/8 | 1/8 | 1/7 | 1/6 | 1/4 | 1/3 | 1/3 | 1 |
Pairwise comparison matrix for multi-criteria decision problems.
Criteria | Slope | Elevation | LULC | Soil moisture | Soil | Geomorphology | Drainage | Geology | Aspect | Weights |
---|---|---|---|---|---|---|---|---|---|---|
Slope | 0.319 | 0.426 | 0.326 | 0.248 | 0.223 | 0.252 | 0.207 | 0.198 | 0.184 | 0.264 |
Elevation | 0.159 | 0.213 | 0.326 | 0.248 | 0.223 | 0.209 | 0.207 | 0.173 | 0.163 | 0.214 |
LULC | 0.159 | 0.106 | 0.163 | 0.330 | 0.278 | 0.168 | 0.177 | 0.173 | 0.163 | 0.190 |
Soil moisture | 0.105 | 0.070 | 0.040 | 0.083 | 0.167 | 0.168 | 0.148 | 0.149 | .0143 | 0.119 |
Soil | 0.079 | 0.053 | 0.032 | 0.027 | 0.056 | 0.126 | 0.118 | 0.124 | 0.122 | 0.082 |
Geomorphology | 0.054 | 0.043 | 0.040 | 0.020 | 0.018 | 0.042 | 0.089 | 0.099 | 0.081 | 0.054 |
Drainage | 0.045 | 0.030 | 0.027 | 0.016 | 0.014 | 0.014 | 0.029 | 0.049 | 0.061 | 0.034 |
Geology | 0.039 | 0.030 | 0.023 | 0.014 | 0.011 | 0.010 | 0.015 | 0.025 | 0.061 | 0.025 |
Aspect | 0.035 | 0.027 | 0.020 | 0.012 | 0.009 | 0.010 | 0.009 | 0.008 | 0.020 | 0.018 |
Normalized pairwise comparison matrix for multi-criteria decision making.
Maximum eigenvalue (
Consistency index (CI) = (
Random index (RI) = 1.46.
Consistency ratio (CR) = (CI/RI) = 0.0676 < 0.10.
Procedure followed in generating agricultural land use suitability map.
Weighted Overlay Analysis was carried out to generate the land suitability for agriculture in the Northern region India district using the weight values of selected factors derived from the Analytic Hierarchy Process and specified scores of sub-criteria (Table 16). Land suitability for agriculture is classified into five levels, according to the Food and Agricultural Organization (FAO): highly suitable agricultural land, moderately suitable agricultural land, marginally suitable agricultural land, land currently not suitable for agriculture, and permanently not suitable for agricultural production (Table 17).
Main criteria | Weight | Influence (%) | Sub-criteria | Score |
---|---|---|---|---|
Slope | 0.264 | 26.4 | 0–3 | 10 |
3–6 | 8 | |||
6–12 | 6 | |||
12–18 | 4 | |||
18–36 | 2 | |||
36–58 | 1 | |||
Elevation | 0.213 | 21.4 | 224–300 | 10 |
300–400 | 9 | |||
400–550 | 8 | |||
550–700 | 7 | |||
700–960 | 5 | |||
LULC | 0.190 | 19 | Crop Land | 10 |
Shrub Land | 4 | |||
Wasteland | 3 | |||
Evergreen Broad leaf Forest | Restricted | |||
Mixed Forest | Restricted | |||
Deciduous Broadleaf Forest | Restricted | |||
Built up Area | Restricted | |||
Permanent wetland | Restricted | |||
Water bodies | Restricted | |||
Soil Moisture | 0.119 | 11.9 | Good Soil Moisture | 10 |
Medium Soil Moisture | 7 | |||
Less Soil Moisture | 4 | |||
Very Less and Dry Soil Moisture | 1 | |||
Water Bodies | Restricted | |||
Soil | 0.082 | 8.2 | Alfisol (alluvial soil) | 9 |
Entisol (bhabar soil) | 6 | |||
Ultisol (brown, red clay soil) | 3 | |||
Geomorphology | 0.054 | 5.4 | Younger Alluvial plain | 10 |
Older Alluvial Plain | 9 | |||
Older Flood plain | 8 | |||
Low and moderated Dissected Hills | 3 | |||
Active Flood plain | 2 | |||
Channel Island / Bar | 1 | |||
Highly Dissected Hills and Valleys | 1 | |||
Water bodies | Restricted | |||
Drainage | 0.034 | 3.4 | High | 9 |
Medium | 7 | |||
Low | 4 | |||
Geology | 0.025 | 2.5 | Quaternary | 9 |
Pliocene - Pleiostocene | 7 | |||
Miocene - Pliocene | 5 | |||
Miocene | 3 | |||
Aspect | 0.018 | 1.8 | South, Southwest, Southeast | 9 |
East, West | 5 | |||
Northeast, Northwest | 4 | |||
North | 2 |
Weights of the criteria and scores of the sub-criteria.
Suitability level | Suitable areas for agricultural production | |
---|---|---|
Area(ha) | % | |
High suitability | 390442.28 | 41.2 |
Moderate suitability | 13498.76 | 14.3 |
Marginally suitable | 3993 | 4.2 |
Currently not suitable | 1766.6 | 1.9 |
Permanently not suitable | 36372.6 | 38.4 |
Areal and percentile distribution of agricultural land suitability analysis results.
High altitude (224–960 m), high slope (3–58) with higher gully erosion intensity, and less drainage availability of the study area were significant factors, resulting in a smaller area or lower rate of highly appropriate agricultural land in Northern region India (Figure 13 and Table 18).
Agriculture land suitability map of northern region India.
Suitability level | Suitable areas for agricultural production | Land qualities/characteristics | Remarks | |
---|---|---|---|---|
Area (ha) | % | |||
High suitability | 390442.28 | 41.2 | Gentle slopes (0–3) with gullies, high soil moisture with lower elevation, alluvial soil, good drainage capacity | Most suitable for agriculture, favorable area for intensive agriculture if irrigation facilities are available |
Moderate suitability | 13498.76 | 14.3 | Gentle to stiff slopes (3–10) with micro terracing, medium soil moisture with lower elevation, moderate drainage capacity | Suitable land for farming practices with proper management, suitable for terrace cultivation |
Marginally suitable | 3993 | 4.2 | (10–20) slope, less soil moisture with higher elevation, coarse loamy to gravel loamy soil, low drainage availability | Less suitable land for agriculture with careful farm management, necessary protections from drainage and intensive erosion |
Currently and permanently not suitable | 38139.2 | 40.3 | Precipitous slope with rocky lands, dry soil, dense forest, barren land, loamy skeletal soil, no drainage availability | The land is not suitable for agriculture, areas under dense vegetation, settlement, barren lands, open rocks are not suitable for agriculture |
Land suitability levels and their land characteristics.
The primary goal of this research was to identify potential agricultural land in the Northern region India district. For the evaluation, an analytical hierarchy approach with a combination of geographic information systems (GIS) was used, and nine different criteria were chosen. The Analytic Hierarchy Process with GIS Integration was shown to be quite useful in determining the best agricultural site. Only 41.2% (39044.28 ha) of the study area was largely suitable for farming at the end of the evaluation, while 40.3% (38139.2 ha) was permanently and temporarily unsuitable for agricultural production. However, inefficient production problems are caused by geomorphological qualities such as very high elevation, steep slope, reduced soil moisture, the presence of bare rocks, and a lack of irrigation system availability. As a result of all these concerns, a moderate quantity of land in the study district has been identified as appropriate for agricultural production. The established result can be implemented into the agricultural production decision-making process in the study area, as it provides insight into determining suitable sites. By critically assessing the procedures and approaches used, the results can be more precise. Physical elements (topographical properties, soil and geological characteristics, etc.) are only part of the analysis, which must also include economic and social conditions for agricultural production. Because the pairwise comparison approach is based on expert judgments, which are primarily subjective in nature, it is used in the analytic hierarchy process. As a result, any incorrect judgment on any of the selected factors can be effectively communicated to the score assignment and weight designation. This is the main disadvantage of the analytic hierarchy approach; hence, weights and scores must be carefully chosen [32, 33]. For more helpful and accurate results, the study should focus on a few key species, such as several therapeutic plants and species that have substantial economic worth and also influence the advancement of rural tourism. The use of very high-resolution satellite images will aid in the assessment of finer areas. Before the ultimate implementation, the indicated locations must also be documented on the ground with various other local and regional parameters.
Authors are very much grateful of the Director, Defense Research & Development Organization, (DRDO) Govt. of India, New Delhi, for providing infrastructure and facilities during the work was carried out.
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In the case of nonbiodegradable inorganic compounds, bioremediation takes the form of bioaccumulation or conversion of one toxic species to a less toxic form for example Cr(VI) is converted to less toxic (III). Bioremediation is considered an environmentally friendly way for pollution clean-up. Microbial clean up can be applied in situ (in place of contamination) or ex situ (off the site of contamination). In situ remediation in the natural environment is deemed slow and often times difficult to control and optimize the different parameters affecting the bioremediation. To this end, use of engineered bioreactors is preferred. Engineered bioreactors providing for optimum conditions for microbial growth and biodegradation have been developed for use in bioremediation processes to achieve the different desired remediation goals. Bioreactors in use range in mode of operation from batch, continuous, and fed batch bioreactors and are designed to optimize microbial processes in relationship to contaminated media and nature of pollutant. Designed bioreactors for bioremediation range from packed, stirred tanks, airlift, slurry phase, and partitioning phase reactors amongst others.",book:{id:"7727",slug:"biotechnology-and-bioengineering",title:"Biotechnology and Bioengineering",fullTitle:"Biotechnology and Bioengineering"},signatures:"Memory Tekere",authors:[{id:"231753",title:"Prof.",name:"Memory",middleName:null,surname:"Tekere",slug:"memory-tekere",fullName:"Memory Tekere"}]},{id:"66868",title:"Structural Design, Fabrication and Evaluation of Resorbable Fiber-Based Tissue Engineering Scaffolds",slug:"structural-design-fabrication-and-evaluation-of-resorbable-fiber-based-tissue-engineering-scaffolds",totalDownloads:1166,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"The use of tissue engineering to regenerate viable tissue relies on selecting the appropriate cell line, developing a resorbable scaffold and optimizing the culture conditions including the use of biomolecular cues and sometimes mechanical stimulation. This review of the literature focuses on the required scaffold properties, including the polymer material, the structural design, the total porosity, pore size distribution, mechanical performance, physical integrity in multiphase structures as well as surface morphology, rate of resorption and biocompatibility. The chapter will explain the unique advantages of using textile technologies for tissue engineering scaffold fabrication, and will delineate the differences in design, fabrication and performance of woven, warp and weft knitted, braided, nonwoven and electrospun scaffolds. In addition, it will explain how different types of tissues can be regenerated by each textile technology for a particular clinical application. The use of different synthetic and natural resorbable polymer fibers will be discussed, as well as the need for specialized finishing techniques such as heat setting, cross linking, coating and impregnation, depending on the tissue engineering application.",book:{id:"7727",slug:"biotechnology-and-bioengineering",title:"Biotechnology and Bioengineering",fullTitle:"Biotechnology and Bioengineering"},signatures:"Martin W. King, Jiyang Chen, Monica Deshpande, Ting He, Harshini Ramakrishna, Yu Xie, Fan Zhang and Fan Zhao",authors:[{id:"237132",title:"Prof.",name:"Martin",middleName:null,surname:"W. King",slug:"martin-w.-king",fullName:"Martin W. 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After a simple deacetylation procedure, chitin is converted into chitosan that consists in a polysaccharide structure of deacetylated-β-glucosamine. Chitosan has been largely employed in wastewater treatment the removal of colloids through coagulation-flocculation processes. Different chitosan based materials have been produced and tested in the removal of inorganic pollutants such as toxic metals and metalloids, nutrients, dyes, micropollutants and hydrocarbons. Sorbents such as magnetic-activated carbon chitosan have been successfully tested in the removal of antibiotics (ciprofloxacin, erythromycin and amoxicillin) from water. Raw chitosan and ZnO nanoparticles entrapped in chitosan have demonstrated an excellent potential for the removal of the insecticide permethrin from aqueous effluents. Chitin and chitosan in flake and powder form have also demonstrated a promising effectiveness in the removal of oil spilled in seawater. Superhydrophobic and superoleophilic sponges modified by thioles have been also prepared from chitosan and used for the removal of oil spills. Chitosan hydrogels have been tested as well as entrapment matrices for the immobilization of hydrocarbon-degrading biomass for oil spills. Strains such as R. corynebacteriorides (QBTo), Bacillus subtilis LAMI008 and B. pumilus have been successfully immobilized and employed in hydrocarbon degradation processes. In this book chapter, the use of chitosan and chitosan-based materials in the removal of organic pollutants from water is reviewed.",book:{id:"6518",slug:"chitin-chitosan-myriad-functionalities-in-science-and-technology",title:"Chitin-Chitosan",fullTitle:"Chitin-Chitosan - Myriad Functionalities in Science and Technology"},signatures:"Carlos Escudero-Oñate and Elena Martínez-Francés",authors:[{id:"188725",title:"Dr.",name:"Carlos",middleName:null,surname:"Escudero-Oñate",slug:"carlos-escudero-onate",fullName:"Carlos Escudero-Oñate"},{id:"246684",title:"MSc.",name:"Elena",middleName:null,surname:"Martínez-Francés",slug:"elena-martinez-frances",fullName:"Elena Martínez-Francés"}]},{id:"60805",title:"Chitosan: A Good Candidate for Sustained Release Ocular Drug Delivery Systems",slug:"chitosan-a-good-candidate-for-sustained-release-ocular-drug-delivery-systems",totalDownloads:1708,totalCrossrefCites:2,totalDimensionsCites:8,abstract:"This chapter focuses on the eye, one of the most important organs of humans. Current data on pathophysiology of the human eye are presented in direct correlation with a range of therapeutic products, with a well-known and widely used material, namely chitosan. Applications of chitosan biopolymer are described in the development of innovative, modern, therapeutic devices and solutions. Thus, chitosan is a good excipient either for classic drop-type ocular systems, as well as for complex drug systems such as nanostructures (nanoparticles, nanomicelles and nanosuspensions), liposomes, microemulsions, microspheres, in situ hydrogels and inserts or implants. 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