Rifaat M. Abdalla

By Rifaat Abdalla

Substantial work has been done by Geospatial Information and Communications Technology (GeoICT) and Disaster Management communities to evaluate and develop tools and applications that integrate the complex interrelationships that are required for adequate preparedness, planning, mitigation, response, and recovery from extreme situations. GeoICT technologies have contributed and are contributing to saving life and property throughout the globe. Over the past decade, extensive research has resulted in more advanced GeoICT technologies. This has helped to maximize the demand for these tools, with a noticeable pattern of adoption and expanding user community. This chapter provides an overview of selected rising stars in GeoICT technology and their applications in disaster management. This discussion evaluates the trends in technology development, with emphasis on data collection, processing, and visualization.

Part of the book: Crisis Management

By Rifaat Abdalla and Salim Al-Harbi

The fast pace of technology development and voluntary adoption of international standards requires interdisciplinary and skill-based education. This chapter presents an approach for the development of an interdisciplinary, internationally recognized geomatics program, at King Abdulaziz University (KAU), using a multilevel approach that combined the international guidelines with the local stakeholders’ needs being in line with the global demand for professionals in this field. The methodology of this study consisted of interviews with subject matter experts (SMEs), students survey and operational analysis, and observation was used to analyze the program challenges and opportunities. Results obtained showed that the transferability of the approach adopted in this research, along with the commonality of lessons learned from the process, contributes to faster execution for similar programs in various parts of the world. The program was successful to secure to international recognition within 10 years of its inception. The quality of learning outcomes supported by the high employability of graduates was among the key socioeconomic impact of the program.

Part of the book: Trends in Geomatics

By Rifaat Abdalla

Part of the book: Trends in Geomatics

By Mohammed El-Diasty and Rifaat Abdalla

The Digital Elevation Model (DEM) can be created using airborne Light Detection And Ranging (LIDAR), Image or Synthetic-Aperture Radar (SAR) mapping techniques. The direct georeferencing of the DEM model is conducted using a GPS/inertial navigation system. The airborne mapping system datasets are processed to create a DEM model. To develop an accurate DEM model, all errors should be considered in the processing step. In this research, the errors associated with DEM models are investigated and modeled using Principal Component Analysis (PCA) and the least squares method. The sensitivity analysis of the DEM errors is investigated using PCA to define the significant GPS/inertial navigation data components that are strongly correlated with DEM errors. Then, the least squares method is employed to create a functional relationship between the DEM errors and the significant GPS/inertial navigation data components. The DEM model errors associated with airborne mapping system datasets are investigated in this research. The results show that the combined PCA analysis and least squares method can be used as a powerful tool to compensate the DEM error due to the GPS/inertial navigation data with about 27% in average for DEM errors produced by the direct georeferenced airborne mapping system.

Part of the book: Time Series Analysis

By Rifaat Abdalla

The oil and gas industry is undergoing a significant transformation with the advent of digitalization and automation technologies. This chapter explores the impact of digitalization and automation on drilling and completion operations in oil and gas wells. The integration of advanced technologies, such as artificial intelligence, machine learning, and robotics, has revolutionized the way wells are planned, drilled, and completed. Digitalization has enabled real-time data acquisition, analysis, and visualization, allowing operators to make informed decisions and optimize drilling and completion processes. Automated systems, including robotic drilling and remotely operated equipment, have enhanced operational efficiency, safety, and cost-effectiveness. The chapter discusses the implementation of digital twin models for virtual well planning and simulation, as well as the use of autonomous drilling systems and smart completion technologies. Moreover, the chapter addresses the challenges and opportunities associated with digitalization and automation, such as data security, workforce reskilling, and the need for collaboration across the industry. It emphasizes the potential for improved well performance, reduced environmental impact, and enhanced reservoir management through the integration of digitalization and automation in oil and gas wells.

Part of the book: Advances in Oil and Gas Well Engineering

By Rifaat Abdalla and Imen Hamdi Nasr

The global COVID-19 pandemic, affecting over 8 million people across 100 nations, presents a severe risk to human life and property. India, with its vast population of 1.34 billion, is among the hardest-hit countries. This study employs machine learning techniques for visualizing and predicting the spatiotemporal progression of COVID-19. Utilizing Python libraries such as “Numpy,” “Pandas,” “Scikit,” and “Matplotlib,” we analyze and visualize COVID-19 data sourced from the Indian Ministry of Health Web Service and API. Our visualizations depict demographic trends, incident growth, geospatial state-based patterns, and distribution. The analysis reveals that age groups under 30 and over 59 exhibit resilience to the virus, offering hope for population growth. Examining active cases, recoveries, and deaths, India has outpaced countries like Germany, the United States, Iran, Italy, Spain, South Korea, Turkey, France, and the United Kingdom since early April 2020. Furthermore, we employ supervised machine learning algorithms, including PROPHET and ARIMA, to predict the virus’s spread. By accounting for seasonality-related factors, we achieve a 95% prediction interval, indicating the potential for accurate spread forecasting. This research contributes valuable insights into COVID-19’s impact in India and offers predictive tools for managing its progression.

Part of the book: Geographic Information Systems

By Rifaat Abdalla

The chapter provides a conceptual model rather than a complete analysis case study-based approach to comprehensively assess interdependencies within the oil and gas sector. Delving into the intricate connections among pipelines, refineries, drilling operations, and transportation networks, the chapter elaborates on the utility of advanced GIS and remote sensing techniques rather than employing them directly in assessing interdependencies within the oil and gas sector. Emphasizing the significance of collaborative data sharing, the chapter visualizes relationships and elucidates the imperative of proactive management practices to fortify resilience. Through detailed case studies and practical instances, it vividly illustrates the possible tangible outcomes of such analysis, offering valuable insights into emergency preparedness, risk mitigation, and resource allocation optimization within the industry. Catering to professionals, researchers, and stakeholders navigating the complex web of dependencies within oil and gas infrastructure, this chapter serves as an essential resource. It ensures stability and uninterrupted functionality, even in the face of unforeseen emergencies and disruptions, by facilitating a profound understanding of the connections and aiding in the implementation of effective strategies to manage them effectively.

Part of the book: Liquefied Petroleum Gas - Recent Advances and Technologies for Energy Transition [Working title]

By Rifaat Abdalla

As the specter of climate change looms over urban agglomerations, this concept chapter delves into the transformative potential of GIS and Remote Sensing techniques in dissecting and mitigating its impacts. By intricately analyzing land-cover and surface temperature data, we unveil the nuanced effects of climate change on land surface temperature (LST) across varied land-cover types. Leveraging the expansive spatial coverage of remote sensing data, especially satellite images, we can meticulously monitor urban structures, offering invaluable insights into impervious surfaces and vegetated areas. This trove of information not only enlightens the current state and evolution of urban structures but also becomes the bedrock for effective urban planning strategies and climate change adaptation measures. In tandem, the amalgamation of remote sensing with GIS techniques facilitates a granular exploration of the intra-urban thermal environment and the intricate spatial links between urban vulnerability and characteristics. By delving into these insights, GIS and remote sensing emerge as indispensable allies in the quantification and monitoring of climate change impacts on urban agglomerations, guiding decisive measures for sustainable urban development and climate adaptation.

Part of the book: Urban Agglomeration - Extracting Lessons for Sustainable Development [Working title]