Large scale developmental projects firstly require the selection of one or more cities to be developed. In Libya, the selection process is done by selected organizations, which is highly influenced by human judgement that can be inconsiderate of socioeconomic and environmental factors. In this study, we propose an automated selection process, which takes into consideration only the important factors for city (cities) selection. Specifically, a geospatial decision-making tool, free of human bias, is proposed based on the fuzzy overlay (FO) and technique for order performance by similarity to ideal solution (TOPSIS) techniques for development projects in Libya. In this work, a dataset of 17 evaluation criteria (GIS factors) across five urban conditioning factors were prepared. The dataset served as input to the FO model to calculate weights (importance) for each criterion. A support vector machine (SVM) classifier was then trained to refine weights from the FO model. TOPSIS was then applied on the refined results to rank the cities for development. Experimental results indicate promising overall accuracy and kappa statistics. Our findings also show that highest and lowest success rates are 0.94 and 0.79, respectively, while highest and lowest prediction rates are 0.884 and 0.673, respectively.
Part of the book: Sustainability in Urban Planning and Design