Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland...

The Kosi Megafan, located in the Himalayan foreland basin, is highly susceptible to devastating floods, posing significant threats to lives and livelihoods. Accurate flood susceptibility mapping is crucial for effective flood risk management in this dynamic environment. This study evaluates and optimizes five advanced machine learning algorithms - Random Subspace, J48, Maximum Entropy (MaxEnt), Artificial Neural Network (ANN-MLP), and Biogeography-Based Optimization- for flood susceptibility zonation within the Kosi Megafan. A comprehensive dataset incorporating 19 conditioning factors, derived from ALOS PALSAR DEM, Sentinel-2A, Landsat 5 TM, ENVISAT-1 ASAR (ENVISAT-1 Advanced Synthetic Aperture Radar), and other ancillary data sources, was used to train and validate the models. Model performance was assessed using a suite of metrics, including accuracy, true skill statistics (TSS), sensitivity, specificity, Kappa, AUC, and the Seed Cell Area Index. Notably, the ANN-MLP model demonstrated exceptional performance on the validation dataset, achieving an accuracy of 0.982, TSS of 0.964, and Kappa of 0.964, outperforming the other models. MaxEnt also exhibited strong performance, confirming its robustness in environmental modeling. The analysis of variable importance revealed that normalized difference vegetation index (NDVI), altitude, distance to road, rainfall, and distance to river were the most influential factors governing flood susceptibility in the region. The generated flood susceptibility maps, particularly those derived from the ANN-MLP and MaxEnt models, provide valuable tools for identifying high-risk areas and informing flood mitigation strategies. This study highlights the potential of advanced machine learning techniques, especially ANN-MLP, in significantly improving the accuracy and reliability of flood susceptibility assessments in complex and dynamic environments like the Kosi Megafan, paving the way for more effective flood risk management and disaster preparedness.