Flood susceptibility assessment using deep neural networks and open-source spatial datasets in transboundary river basin

The Mekong Basin is the most critical transboundary river basin in Asia. This basin provides an abundant source of fresh water essential for the development of agriculture, domestic consumption, and industry, as well as for the production of hydroelectricity, and it also contributes to ensuring food security worldwide. This region is often subject to floods that cause significant damage to human life, society, and the economy. However, flood risk management challenges in this region are increasingly substantial due to conflicting objectives between several countries and data sharing. This study integrates deep learning with optimization algorithms, namely Grasshopper Optimisation Algorithm (GOA), Adam and Stochastic Gradient Descent (SGD), and open-source datasets to identify the region of probably occurring floods in the Mekong basin, covering Vietnam and Cambodia. Various statistical indices, namely Area Under the Curve (AUC), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), were used to evaluate flood susceptibility models. The results show that the proposed models performed well with AUC values above 0.8, specifying that the DNN-Adam model achieved an AUC of 0.98, outperforming DNN-GOA (AUC = 0.89), DNN-SGD (AUC = 0.87), and XGB (AUC = 0.82. Regions with very high flood susceptibility are concentrated in the Mekong Delta of Vietnam and along the Mekong River in Cambodia. The findings of this study are significant in supporting decision-makers or planners in proposing appropriate flood mitigation strategies, planning policies, and strategies, particularly in the Mekong River watershed.