Abstract
The Kingdom of Saudi Arabia is undergoing massive and rapid urbanization as part of Vision 2030. This includes development projects along Saudi Arabia’s coastline across the Red Sea. Coastal areas, especially the ones along Saudi’s western regions are susceptible to natural disasters such as flooding. NEOM, a futuristic city currently being developed in the northwest of Saudi Arabia, exemplifies a potential flooding hazard due to its geographic location and proposed urbanization plans. This research aims to enhance flood hazard assessment in NEOM by applying the Fuzzy Analytical Hierarchy Process (FAHP) in combination with Geographic Information System (GIS). Acknowledging traditional limitations related to data availability and parameter selection consensus, the study carefully selects parameters such as drainage density, elevation, slope, rainfall, land use/land cover (LULC), soil type, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The 30 m DEM was used to derive Drainage Density, Slope, and TWI while LULC data helped assess land cover changes. Rainfall data and soil type information are integrated to evaluate their impact on flood susceptibility. NDVI is employed to analyze vegetation cover. Utilizing ArcGIS Pro’s weighted overlay model, the criteria were combined to generate the final flood susceptibility map. The research outcomes manifest in a flood susceptibility map categorizing areas into seven distinct susceptibility classes, ranging from ‘very low’ to ‘very high.’ A quantitative breakdown in a summary table provides insights into the proportional distribution of flood risk. Results indicate a significant portion of NEOM falls within varying degrees of moderate susceptibility range with relatively limited distribution of flood susceptibility on the extremes, equating to areas with ‘low to moderate’ susceptibility is 4,322.8 km2, areas with ‘moderate’ susceptibility is 5,109.69 km2, areas with ‘moderate to high’ is 4,081.39 km2. The flood susceptibility map developed in this study can shed insights on potential optimum areas for flood mitigation measures (i.e., optimum locations for establishing stormwater collection points).
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