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Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model

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Abstract

The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is investigated in rainfall-runoff modeling at hourly timescale. The results are compared with the conceptual EBA4SUB model using rainfall and runoff data from Samoggia River basin, Italy. The capability of the methods is measured using five statistics, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error, scatter index, and adjusted index of agreement. Comparison of single ML reveals that the ANFIS-PSO, ANFIS-FCM and MARS produce similar accuracy which is better than the M5Tree model. MM-SA ensemble model improves the accuracy of ANFIS-PSO, ANFIS-FCM, MARS and M5Tree models with respect to RMSE by 8.5%, 5%, 7.4% and 28.8%, respectively. Comparison with the conceptual event-based method indicates that the ML methods generally performs superior to the EBA4SUB; however, latter method provides better accuracy than the M5Tree and MARS in some cases.

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Acknowledgements

The authors are deeply grateful to Attilio Castellarin, University di Bologna, DICAM Department, Italy, for having provided 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, Nanjing, 210098, China

    Rana Muhammad Adnan

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

    Andrea Petroselli

  3. Agronomy Department, Faculty of Science, 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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  2. Andrea Petroselli
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  3. Salim Heddam
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  4. Celso Augusto Guimarães Santos
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  5. Ozgur Kisi
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Correspondence to Andrea Petroselli or Ozgur Kisi.

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Adnan, R.M., Petroselli, A., Heddam, S. et al. Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stoch Environ Res Risk Assess 35, 597–616 (2021). https://doi.org/10.1007/s00477-020-01910-0

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