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

Author

Listed:
  • Rana Muhammad Adnan

    (Hohai University)

  • Andrea Petroselli

    (University of Tuscia)

  • Salim Heddam

    (Faculty of Science, Agronomy Department, Hydraulics Division)

  • Celso Augusto Guimarães Santos

    (Federal University of Paraíba)

  • Ozgur Kisi

    (Ilia State University
    Duy Tan University)

Abstract

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 swapping 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. Graphic abstract

Suggested Citation

  • Rana Muhammad Adnan & Andrea Petroselli & Salim Heddam & Celso Augusto Guimarães Santos & Ozgur Kisi, 2021. "Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2987-3011, February.
    • Handle: RePEc:spr:nathaz:v:105:y:2021:i:3:d:10.1007_s11069-020-04438-2
    DOI: 10.1007/s11069-020-04438-2
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    References listed on IDEAS

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    1. Krishna Singh & Mahesh Pal & V. Singh, 2010. "Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2007-2019, August.
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    3. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    4. Rana Muhammad Adnan & Xiaohui Yuan & Ozgur Kisi & Muhammad Adnan & Asif Mehmood, 2018. "Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4469-4486, November.
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    Cited by:

    1. Huiqi Deng & Wenjie Chen & Guoru Huang, 2022. "Deep insight into daily runoff forecasting based on a CNN-LSTM model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1675-1696, September.
    2. 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.
    3. Amir Molajou & Vahid Nourani & Abbas Afshar & Mina Khosravi & Adam Brysiewicz, 2021. "Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2369-2384, June.

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