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Prediction of river flow using hybrid neuro-fuzzy models

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Abstract

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACOR), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R2, root mean square error, RMSE (m3/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.

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Acknowledgements

The authors would like to thank the Esfahan Regional Water Authority for providing the necessary data to carry out this investigation.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Semnan University, Semnan, Iran

    Armin Azad, Saeed Farzin & Hojat Karami

  2. Depatment of Plant science, Technology University of Munich, Weihenstephan Campus, Freising, Munich, Germany

    Hamed Kashi

  3. Department of Water Sciences and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

    Hadi Sanikhani

  4. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

Authors
  1. Armin Azad
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  2. Saeed Farzin
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  3. Hamed Kashi
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  4. Hadi Sanikhani
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  5. Hojat Karami
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  6. Ozgur Kisi
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Correspondence to Saeed Farzin.

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Azad, A., Farzin, S., Kashi, H. et al. Prediction of river flow using hybrid neuro-fuzzy models. Arab J Geosci 11, 718 (2018). https://doi.org/10.1007/s12517-018-4079-0

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