Multi Model Approach for Site Suitability Predictive Modelling for Urban Change and Assessment of Sustainable Development Goal 11 in Latakia Syria

Abstract

Urbanization presents significant challenges globally, increasing the need for enhanced newlineurban services and the development of new residential areas. This process often correlates with newlinenatural disasters, exacerbating problems such as pollution, socioeconomic disparities, urban newlineflooding, and political instability, particularly in developing nations. As cities expand, newlinecomprehensive planning becomes essential to improve quality of life and develop new urban newlineareas. Identifying suitable locations for urban development is crucial, and satellite imagery is newlineinstrumental in monitoring land use changes and urban sprawl. Geographic Information newlineSystems (GIS) and remote sensing technologies provide powerful tools for mapping and newlinemanaging urban growth, offering valuable insights for sustainable development. newlineThis thesis aims to evaluate site suitability for new urban areas using various Multi- newlineCriteria Decision Making (MCDM) techniques, such as Analytic Hierarchy Process (AHP) and newlineBest-Worst Method (BWM), both separately and as hybrid models integrated with bivariate newlineanalysis. Furthermore, it seeks to forecast future Land Use Land Cover (LULC) using newlinemultispectral data, and to assess urban sprawl, urban growth models, and Sustainable newlineDevelopment Goal (SDG) 11 indicators. newlineThe research identified notable deviations between AHP and BWM models in newlinedetermining urban site suitability, with hybrid models producing more aligned results. Remote newlinesensing indices, particularly the Normalized Difference Vegetation Index (NDVI), effectively newlinecaptured vegetation and water bodies. The integration of multiple indices and deep learning newlinemodels significantly improved the accuracy of built-up area extraction newline