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Development of hybrid baseflow prediction model by integrating analytical method with deep learning

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Abstract

In recent years, the success of deep learning in many different fields of Engineering has attracted attention. Baseflow separation is one of the Engineering problems which remains difficult due to different hydro-climatic circumstances. In this study, we proposed a hybrid baseflow prediction model by combining analytical methods and deep learning algorithms. Six analytical methods were chosen and their performance was compared by different metrics. Baseflow-Lyne and Hollick algorithm (BFLOW-LHA) outperforms the others in terms of R2, Mean Absolute Error (MAE), BIAS, Nash–Sutcliffe Efficiency (NSE), and Root Mean Squared Error (RMSE) metrics. The proposed model was trained using streamflow and baseflow data generated by the BFLOW-LHA with the Dawa Melka Guba dataset and then tested on prediction for the basin's remaining three watersheds. The experimental results show that the proposed model improves the prediction of baseflow as compared with BFLOW-LHA and can be used for watersheds with similar characteristics.

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Data availability

All the relevant data are uploaded on GitHub and accessible via the following URL: https://github.com/wondiye8/baseflow

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Acknowledgements

This study was performed by three academic staff at Jimma Institute of Technology, Jimma University, Ethiopia. The authors would like to thank the institute for its assistance with various resources, as well as MoWIE of Ethiopia for providing the dataset for our experiments. The authors would like to thank Jimma University for its support during the research work.

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This study received no outside funding.

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Authors and Affiliations

  1. Faculty of Civil and Environmental Engineering, Jimma University Institute of Technology, Jimma, Ethiopia

    Wondmagegn Taye Abebe

  2. Faculty of Computing and Informatics, Jimma University Institute of Technology, Jimma, Ethiopia

    Demeke Endalie & Getamesay Haile

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  1. Wondmagegn Taye Abebe
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  2. Demeke Endalie
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  3. Getamesay Haile
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Correspondence to Wondmagegn Taye Abebe.

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Abebe, W.T., Endalie, D. & Haile, G. Development of hybrid baseflow prediction model by integrating analytical method with deep learning. Sustain. Water Resour. Manag. 8, 97 (2022). https://doi.org/10.1007/s40899-022-00694-1

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