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An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Author

Listed:
  • Abhinav Kumar Singh

    (Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India)

  • Pankaj Kumar

    (Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India)

  • Rawshan Ali

    (Department of Petroleum, Koya Technical Institute, Erbil Polytechnic University, Erbil 44001, Iraq)

  • Nadhir Al-Ansari

    (Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden)

  • Dinesh Kumar Vishwakarma

    (Department of Irrigation and Drainage Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India)

  • Kuldeep Singh Kushwaha

    (Centre for Water Engineering and Management, Central University of Jharkhand, Ranchi 835205, India)

  • Kanhu Charan Panda

    (Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India)

  • Atish Sagar

    (Division of Agricultural Engineering, ICAR—Indian Agriculture Research Institute, New Delhi 110012, India)

  • Ehsan Mirzania

    (Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran)

  • Ahmed Elbeltagi

    (Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt)

  • Alban Kuriqi

    (CERIS, Instituto Superior Técnico, University of Lisbon, 1649-004 Lisbon, Portugal
    Civil Engineering Department, University for Business and Technology, 10000 Pristina, Kosovo)

  • Salim Heddam

    (Laboratory of Research in Biodiversity 17 Interaction Ecosystem and Biotechnology, Agronomy Department, Hydraulics Division, Faculty of Science, University 20 Août 1955, Route El Hadaik, Skikda 21000, Algeria)

Abstract

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination ( R 2 ), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m 3 /s), R 2 , NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.

Suggested Citation

  • Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8209-:d:856301
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    References listed on IDEAS

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