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River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network

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Abstract

River flow modeling plays an important role in water resources management. This research aims at developing a hybrid model that integrates the feed-forward neural network (FNN) with a hybrid algorithm of the particle swarm optimization and gravitational search algorithms (PSOGSA) to predict river flow. Fundamentally, as the precision of a FNN model is essentially dependent upon the assurance of its model parameters, this review utilizes the PSOGSA for ideal preparing of the FNN model and gives the likelihood of boosting the execution of FNN. For this purpose, monthly river flow time series from 1990 to 2016 for Garber station of the Turkey River located at Clayton County, Iowa, were used. The proposed FNN-PSOGSA was applied in monthly river flow data. The results indicate that the FNN-PSOGSA model improves the forecasting accuracy and is a feasible method in predicting the river flow.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Rani Durgawati University, Jabalpur, India

    Sarita Gajbhiye Meshram & Chandrashekhar Meshram

  2. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Mohmmmad Ali Ghorbani & Vahid Karimi

  3. Engineering Faculty, Near East University, 99138, Nicosia, Mersin 10, North Cyprus, Turkey

    Mohmmmad Ali Ghorbani

  4. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Shahaboddin Shamshirband

  5. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Shahaboddin Shamshirband

Authors
  1. Sarita Gajbhiye Meshram
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  2. Mohmmmad Ali Ghorbani
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  3. Shahaboddin Shamshirband
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  4. Vahid Karimi
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  5. Chandrashekhar Meshram
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Correspondence to Shahaboddin Shamshirband.

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Communicated by V. Loia.

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Meshram, S.G., Ghorbani, M.A., Shamshirband, S. et al. River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network. Soft Comput 23, 10429–10438 (2019). https://doi.org/10.1007/s00500-018-3598-7

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