The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed

Abstract

The Majalaya area is one of the most valuable economic districts in the south of Greater Bandung, West Java, Indonesia, and experiences at least six floods per year. The floods are characterized by a sudden rise in the water level approximately one to two hours after the rain occurs. With the aim of reducing flood risk, this study models a data-driven method for predicting the inundation height across the Majalaya Watershed. The flood inundation maps of selected events were modeled using the HEC-RAS 2D numerical model. Extracted data from the HEC-RAS model, GSMaP satellite rainfall data, elevation, and other spatial data were combined to build an artificial neural network (ANN) model. The trained model targets inundation height, while the spatiotemporal data serve as the explanatory variables. The results from the trained ANN model provided very good R2 (0.9537), NSE (0.9292), and RMSE (0.3701) validation performances. The ANN model was tested with a new dataset to demonstrate the capability of predicting flood inundation height with unseen data. Such a data-driven approach is a promising tool to be developed to reduce flood risks in the Majalaya Watershed and other flood-prone locations.