Flooding poses a significant challenge in Thailand due to its complex geography, traditionally addressed through GIS methods like the Flood Risk Assessment Model (FRAM) combined with the Analytical Hierarchy Process (AHP). This study assesses the efficacy of Artificial Neural Networks (ANN) in flood susceptibility mapping, using data from Ayutthaya Province and incorporating 5-fold cross-validation and Stochastic Gradient Descent (SGD) for training. ANN achieved superior performance with precision of 79.90%, recall of 79.04%, F1-score of 79.08%, and accuracy of 79.31%, outperforming the traditional FRAM approach. Notably, ANN identified that only three factors—flow accumulation, elevation, and soil types—were crucial for predicting flood-prone areas. This highlights the potential for ANN to simplify and enhance flood risk assessments. Moreover, the integration of advanced machine learning techniques underscores the evolving capability of AI in addressing complex environmental challenges.
