Integrating multi-criteria decision analysis and geospatial data for flood susceptibility mapping in Texas, USA

Abstract

Floods are among the most frequent and destructive natural hazards triggered by snowmelt, intense and prolonged precipitation. This study aimed to delineate flood-prone areas across Texas, USA, by integrating geospatial data with a multi-criteria decision analysis (MCDA) approach. The Analytical Hierarchy Process (AHP) was employed within this framework to evaluate systematically and weight key flood conditioning factors. The factor weights in the AHP were established based on insights from expert evaluations, literature, and feedback from relevant public institutions. Flood susceptibility mapping effectiveness was assessed through the Receiver Operating Characteristic (ROC) curve, focusing on the Area under the Curve (AUC) metric. A multi-criteria weighted overlay method was used to combine various geospatial layers. The flood susceptibility map was validated using historical storm event data from the National Centers for Environmental Information (NCEI), covering the period from 1985 to the present. The final susceptibility map achieved a high AUC score of 0.90, reflecting a robust agreement between the model's predictions and real-world flood events. The most flood-vulnerable basins include the Sulphur, Cypress, Trinity, Neches-Trinity, Sabine, Guadalupe, and Neches basins, which stand out as the most at-risk areas identified in the analysis. The spatial analysis of the flood susceptibility map revealed that approximately 62 % of the study area falls under high flood risk. Thus, priority should be given to implementing targeted flood management and mitigation strategies in the high-risk river basins.