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Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data

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Abstract

Accurate estimation of streamflow has a vital importance in water resources engineering, management and planning. In the present study, the abilities of group method of data handling-neural networks (GMDH-NN), dynamic evolving neural-fuzzy inference system (DENFIS) and multivariate adaptive regression spline (MARS) methods are investigated for monthly streamflow prediction. Precipitation, temperature and streamflows from Kalam and Chakdara stations at Swat River basin (mountainous basin), Pakistan, are used as inputs to the applied models in the form of different input scenarios, and models’ performances are evaluated on the basis of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and combined accuracy (CA) indexes. Test results of the Kalam Station show that the DENFIS model provides more accurate prediction results in comparison of GMDH-NN and MARS models with the lowest RMSE (18.9 m3/s), MAE (13.1 m3/s), CA (10.6 m3/s) and the highest NSE (0.941). For the Chakdara Station, the MARS outperforms the GMDH-NN and DENFIS models with the lowest RMSE (47.5 m3/s), MAE (31.6 m3/s), CA (26.1 m3/s) and the highest NSE (0.905). Periodicity (month number of the year) effect on models’ accuracies in predicting monthly streamflow is also examined. Obtained results demonstrate that the periodicity improves the models’ accuracies in general but not necessarily in every case. In addition, the results also show that the monthly streamflow could be successfully predicted using only precipitation and temperature variables as inputs.

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Data availability

The data used in this paper were obtained from the Water and Power Development Authority (WAPDA), Pakistan. The meteorological and hydrological data used in this study can be obtained from WAPDA on request as data cannot be publicly accessed (https://www.wapda.gov.pk).

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Acknowledgements

The authors thank to the staff of WAPDA for providing data.

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Authors and Affiliations

  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology & Water Resources, Hohai University, 210098, Nanjing, China

    Rana Muhammad Adnan & Zhongmin Liang

  2. Department of Mathematics, IKG Punjab Technical University, Jalandhar-Kapurthala, Punjab, India

    Kulwinder Singh Parmar

  3. CSIR-National Physical Laboratory, New Delhi, India

    Kirti Soni

  4. Civil engineering department, Ilia state university, Tbilisi, Georgia

    Ozgur Kisi

  5. 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. Zhongmin Liang
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  3. Kulwinder Singh Parmar
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  4. Kirti Soni
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  5. Ozgur Kisi
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Correspondence to Rana Muhammad Adnan or Ozgur Kisi.

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Adnan, R.M., Liang, Z., Parmar, K.S. et al. Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data. Neural Comput & Applic 33, 2853–2871 (2021). https://doi.org/10.1007/s00521-020-05164-3

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  • DOI: https://doi.org/10.1007/s00521-020-05164-3

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