Flood Susceptibility Mapping of the Kosi Megafan Using Ensemble Machine Learning and SAR Data

Every year, floods disrupt the lives of hundreds of millions of people worldwide. Their impacts are further intensified by climate change, rapid urbanization, and land-use changes, making it crucial to identify areas most susceptible to flooding. While machine learning (ML) models have proven effective in identifying flood susceptibility, their validity and the integration of human risk remain underexplored in geomorphologically complex and highly flood-prone regions. This study developed an ensemble ML framework for flood susceptibility mapping in the Kosi Megafan, located in Nepal and India. We compared its performance with established ML models and a one-dimensional convolutional neural network (1D-CNN), validated results using Dartmouth Flood Observatory (DFO) and Sentinel-1 SAR (Synthetic Aperture Radar) data, and assessed the population exposed to high-risk zones. A total of 13 (8 retained) flood conditioning factors (FCFs) were derived from remote sensing datasets, and a flood inventory was created to train multiple ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), 1D-CNN, and a Stacked Ensemble model. Among these, the stacked ensemble model achieved the highest performance (AUC = 0.76, accuracy = 0.70, precision = 0.69, recall = 0.72, F1-score = 0.70). The resulting susceptibility map identified high-risk zones mainly in the southern and southwestern Megafan, showing strong spatial agreement with the Sentinel-1-derived flood inventory and the DFO flood data (1992–2022). This study highlights the effectiveness of combining SAR-derived flood evidence with ensemble ML approaches for accurate and scalable flood susceptibility mapping in data-scarce, hazard-prone basins. Ultimately, the research supports efforts to build resilience and mitigate the long-term impact of flooding in the region.