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

ABSTRACT: 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 perf

SUBMITTER: Arora A 

PROVIDER: S-EPMC12460597 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature