Currell, MatthewAlabbas, Mohammed2025-04-152025https://hdl.handle.net/20.500.14154/75191This research presents a flood susceptibility mapping (FSM) study for Tabuk, Saudi Arabia, utilizing artificial intelligence and machine learning techniques. The aim is to identify flood-prone areas to support urban planning and flood risk management in arid environments. The study integrates environmental data such as Digital Elevation Models (DEM), land use and land cover (LULC), and hydrological indices like the Topographic Wetness Index (TWI) and Stream Power Index (SPI). Four machine learning models — Random Forest, Support Vector Regression, Artificial Neural Networks, and Decision Trees — were applied and evaluated for accuracy. The results highlight the significance of low-lying topographies and proximity to streams in flood vulnerability. Despite limitations due to insufficient historical flood data and lack of socio-economic analysis, the study underscores the potential of AI-based methods in improving flood prediction and resilience planning.This study focused on flood susceptibility mapping (FSM) for Tabuk, Saudi Arabia, using artificial intelligence and advanced techniques in an effort to find areas that are more prone to flooding. The study thus aims at generating a reliable tool for urban planning and flood risk management in a flash-flood-prone arid region. The acquisition of data involved various sources, among others, Digital Elevation Models (DEM), land use and land cover (LULC), hydrological data (Topographic Wetness Index, Stream Power Index), and noted flood records. Four ML models- Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT)-were used to assess the environmental conditions and produce an FSM. They were validated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) to assess their predictive performance. The results seem rather to show that the low-lying areas, proximity to streamlines, and several topographic features contribute significantly to the flood susceptibility of the town. Shortages of historical flood data are one of the limitations that can provide obstacles to the prediction ability of the models used for flood risk assessments, with no consideration given to socio- economic factors. Recommendations for improvement in the relational modeling for better forecast of flood vulnerability include more accurate data, collecting long-term historical records of flood occurrence, and considering socio-economic factors into integrated flood risk models for providing proper flood management plans.55en(Generation of Flood Susceptibility Map Using Artificial Intelligence: A Case StudyTabukKSA)Flood Risk AssessmentMachine Learning (ML)Thesis