Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches

The Karnali River Basin (KRB) comprises the longest river in Nepal, located south of the Himalayas. Despite its high susceptibility to floods, the basin lacks detailed studies. Proper floodplain management is essential to reduce the impacts due to rising flood frequency, magnitude, and severity aggravated by climate change. This research applies three machine learning techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), to flood event data from the KRB. Ten flood conditioning factors; Aspect, curvature, distance to a river (DTR), normalized difference vegetation index (NDVI), elevation, slope, rainfall, soil, stream power index (SPI), and topographical wetness index (TWI) were selected based on the multicollinearity test. The parameter performance was evaluated using the Cohen Kappa Score, with NDVI having the greatest influence, followed by elevation, DTR, curvature, and TWI. Based on the Area Under the Curve of Receiver Operating Characteristics (AUROC), SVM outperformed RF, ANN, and ANN for FSM. The area of very high flood susceptible areas ranges from 0.8 to 2.5% of the basin area, most of them located in the south with low slopes and elevations. The results of this study suggest the use of SVM for FSM to help with proper floodplain management.