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Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach

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Accurate short-term rainfall–runoff prediction is essential for flood mitigation and safety of hydraulic structures and infrastructures. This study investigates the capability of four machine learning methods (MLM), optimal pruning extreme learning machine (OPELM), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree, and hybridized MARS and Kmeans algorithm (MARS-Kmeans), in hourly rainfall–runoff modeling (considering 1-, 6- and 12-h horizons). Their results are compared with a conceptual method, Event-Based Approach for Small and Ungauged Basins (EBA4SUB) and multi-linear regression (MLR). Hourly rainfall and runoff data gathered from Ilme River watershed, Germany, were divided into two equal parts, and MLM were validated considering each part by swap** training and testing datasets. MLM were compared with EBA4SUB using four events and with respect to three statistics, root-mean-square errors (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE). Comparison results revealed that the newly developed hybridized MARS-Kmeans method performed superior to the OPELM, MARS, M5Tree and MLR methods in prediction of 1-, 6- and 12-h ahead runoff. Comparison with conceptual method showed that all the machine learning models outperformed the EBA4SUB and OPELM provided slightly better performance than the other three alternatives in event-based rainfall–runoff modeling.

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Acknowledgements

The authors are deeply grateful to Markus Wallner and Uwe Haberlandt, from the institute of water resources management, hydrology and agricultural hydraulic engineering, Hannover, Germany, for providing the watershed data used in the present manuscript.

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Authors and Affiliations

  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nan**g, 210098, China

    Rana Muhammad Adnan

  2. Department of Economy, Engineering, Society and Business (DEIM), University of Tuscia, Viterbo, Italy

    Andrea Petroselli

  3. Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria

    Salim Heddam

  4. Department of Civil and Environmental Engineering, Federal University of Paraíba, Paraíba, Brazil

    Celso Augusto Guimarães Santos

  5. Civil Engineering Department, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  6. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Ozgur Kisi

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  1. Rana Muhammad Adnan
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Correspondence to Ozgur Kisi.

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Adnan, R.M., Petroselli, A., Heddam, S. et al. Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Nat Hazards 105, 2987–3011 (2021). https://doi.org/10.1007/s11069-020-04438-2

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