Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia

• Published in: Journal of hydrology. Regional studies Vol. 36; p. 100855
• Main Authors: Tamiru, Habtamu, Dinka, Megersa O.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2021; Elsevier
• Subjects: ANNs; basins; Ethiopia; Flood inundation; HEC-RAS; Machine-learning; NDWI; neural networks; prediction; rivers; runoff; time series analysis; TWI; watersheds; Ethiopia; Flood inundation; HEC-RAS; ANNs; NDWI; Machine-learning; TWI
• ISSN: 2214-5818; 2214-5818
• DOI: 10.1016/j.ejrh.2021.100855

[Display omitted] •The novelty of data-driven and HEC-RAS model for flood inundation is presented.•The accuracy in prediction and flood inundation is improved.•14 meteorological stations for 1999−2005 periods and TWI are used in ANN model.•Flood inundation obtained in HEC-RAS model is calibrated and validated in NDWI. Lower Baro River, Ethiopia. This paper presents the novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo Basin River, Ethiopia. ANN and HEC-RAS model is applied and successfully improves the accuracy of prediction and flood inundation in the region. This study uses 14 meteorological stations on a daily basis for 1999−2005 and 2006−2008 periods, and Topographical Wetness Index (TWI) to the train and test the model respectively. The runoff time series obtained in ANN model is linked to HEC-RAS and the flood depths were generated. The flood inundation generated in HEC-RAS model result was calibrated and validated in Normal Difference Water Index (NDWI). As the inundation map generated from the runoff values of ANN model reveals, the lower Baro river forms huge inundation depth up to 250 cm. The performance the ANN model was evaluated using Nash-Sutcliffe Efficiency (NSE = 0.86), PBIAS = 8.2 % and R2 = 0.91 and NSE = 0.88, PBIAS = 8.5 % and R2 = 0.93 during the training and testing periods respectively. The generated inundation areas in HEC-RAS and the water bodies delineated in NDWI were covered with 94.6 % and 96 % as overlapping areas during the calibration and validation periods respectively. Therefore, it is concluded that the integration of the ANN approach with the HEC-RAS model has improved the prediction accuracy in traditional flood forecasting methods.