Spatial Flash Flood Modeling in the Beas River Basin of Himachal Pradesh, India, Using GIS-Based Machine Learning Algorithms

Flash flood is a significant issue in the Beas river basin. The main objectives of this study are to map flash flood susceptibility (FFS) in the Beas River Basin of Himachal Pradesh using a random forest (RF) data-driven model, prioritie flash flood conditioning factors using this methodology, and compare it with a multivariate adaptive regression spline (MARS).To provide the best prediction performance, their ensemble (MARS-RF) is used. The findings demonstrated that while predicting the FFS, the MARS, RF, and ensemble models obtained corresponding areas under curves (AUC) of 0.828, 0.856, and 0.88. The ensemble approach, which bases priority determination on the best model, was discovered to have a significant sensitivity to flash floods. It was found that 11.81% of the total area was extremely susceptible to flash flood events. These zonations employing RS-GIS and machine learning models can help decision-makers take quick and effective action to reduce flash floods and lessen the possibility of severe loss of life and property.