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Model estimations of flood susceptibility often face challenges in assigning appropriate weights to factors that contribute to flooding. This study focuses on evaluating flood-prone areas in Kota Setar and Padang Terap, Kedah, Malaysia, by applying optimum weights to proposed flood parameters using the Analytical Hierarchy Process (AHP) model integrated with a Geographic Information System (GIS). Physical features, including slope, elevation, topographic wetness index (TWI), and flow accumulation, were extracted using Interferometric Synthetic Aperture Radar (IFSAR) data, alongside land use and rainfall data. For validation, approximately 1,279 historical flood marks from the Department of Irrigation and Drainage (DID) were used. The resulting flood susceptibility map and historical flood data aligned well with an RMSE of 92.2% after weighted flood parameter integration. The most significant contributors to flood susceptibility were identified as slope (39%), land use (21%), rainfall (19%), and elevation (11%). The findings indicate that that optimizing parameters with weights improves susceptible area prediction for future flood mapping research using AHP and GIS.
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Imani et al. (2025) studied this question.
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