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Applicability of machine learning techniques for multi-time step ahead runoff forecasting

  • Research Article - Hydrology
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Abstract

Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t + 1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R2) values of 168.94 m3/s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R2 values were observed to be 169.72 m3/s, 0.67, 0.84, 0.7023 and 148.55 m3/s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis.

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The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this manuscript further.

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Authors and Affiliations

  1. Department of Soil Science and Agriculture Chemistry, School of Agriculture, Lovely Professional University, Phagwara, 144411, India

    Tarate Suryakant Bajirao

  2. Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

  3. College of Agricultural Engineering, Dr. R.P.C.A.U., Pusa, Bihar, 848125, India

    Manish Kumar

  4. Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200, Sosnowiec, Poland

    Quoc Bao Pham

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  1. Tarate Suryakant Bajirao
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  2. Ahmed Elbeltagi
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  3. Manish Kumar
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  4. Quoc Bao Pham
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Contributions

Conceptualization, methodology, TSB; software, validation, formal analysis, investigation, TSB, QBP and AE; writing-review and editing, TSB, AE, QBP and MK; visualization, TSB, AE and MK; supervision, QBP, AE and MK.

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Correspondence to Manish Kumar or Quoc Bao Pham.

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Edited by Dr. Mohammad Valipour (ASSOCIATE EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

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Bajirao, T.S., Elbeltagi, A., Kumar, M. et al. Applicability of machine learning techniques for multi-time step ahead runoff forecasting. Acta Geophys. 70, 757–776 (2022). https://doi.org/10.1007/s11600-022-00749-z

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