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Mapping flood inundation in Baro Akobo Basin, Itang area, Ethiopia: integrating machine learning and process-based models

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Abstract

Accurate flood mapping is essential for assessing flood hazards, particularly in areas like the lower Baro flood plain in Ethiopia where floods pose significant challenges to society. This study aims to enhance flood inundation mapping by integrating the Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Artificial Neural Network (ANN) with the Hydrologic Engineering Centre-River Analysis System (HEC-RAS). Data from 14 meteorological stations and 3 streamflow stations, spanning from 2000 to 2016, including soil characteristics, Digital Elevation Model, and land use data, were used in the analysis. The combination of ANN and HEC-HMS models provided runoff values for input into the HEC-RAS model, resulting in the creation of accurate flood inundation maps. The HEC-HMS-ANN model was evaluated using statistical metrics such as Nash Sutcliffe (NSE), Root Mean Square Error (RMSE), and Correlation coefficient (R²) demonstrating excellent performance with NSE of 0.9924, RMSE of 24 m³/s, and R² of 0.9926. Calibration and validation of flood inundation outputs from HEC-RAS using the Normalized Difference Water Index (NDWI) revealed high accuracy with overlapping percentages of 90.6% and 91% during the calibration and validation phases, respectively. This integration of models significantly enhances prediction accuracy compared to traditional flood forecasting methods in the Gambella gaging station and Itang area.

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Data availability

The data supporting this study’s findings are available on request from the corresponding author.

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Acknowledgements

The authors would like to thank the Ethiopian Ministry of Water Resource (MoWR) and the Ethiopian National Metrological Institute (NMI) for providing us with the data for this study. Additionally, the author would like to thank Haramaya University for providing the opportunity for my PhD studies and for sponsoring my tuition. I want to express my gratitude to the anonymous reviewer for their insightful comments, which helped the paper’s quality greatly.

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This research did not receive any specific funding.

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Authors and Affiliations

  1. Department of Hydraulic and Water Resources Engineering, Haramaya Institute of Technology, Haramaya University, P.O. Box 138, Dire Dawa, Ethiopia

    Yonata Belina & Asfaw Kebede

  2. Department of IT, Central University of Technology, Free State, Private Bag X20539, Bloemfontein, 9301, South Africa

    Muthoni Masinde

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  1. Yonata Belina
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  2. Asfaw Kebede
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Contributions

Asfaw Kebede and Muthoni Masinde reviewed and provided feedback on earlier drafts of the manuscript and Yonata Belina performed material preparation, data collection, and analysis.All authors participated in the study’s conception and design and subsequently reviewed and endorsed the final manuscript.

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Correspondence to Yonata Belina.

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Communicated by Hassan Babaie.

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Belina, Y., Kebede, A. & Masinde, M. Mapping flood inundation in Baro Akobo Basin, Itang area, Ethiopia: integrating machine learning and process-based models. Earth Sci Inform 18, 3 (2025). https://doi.org/10.1007/s12145-024-01547-5

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