Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

Abstract

This study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.