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RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia

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Abstract

Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.

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Acknowledgments

The authors appreciate so much the financial support received by the second and sixth authors via DIP-2012-03 project funded from Universiti Kebangsaan Malaysia.

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Authors and Affiliations

  1. Civil and Structural Engineering Department, Faculty of Engineering and Built Environment, National University of Malaysia, UKM, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Zaher Mundher Yaseen, Ahmed El-Shafie, Haitham Abdulmohsin Afan, Mohammed Hameed & Wan Hanna Melini Wan Mohtar

  2. Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering, National University of Malaysia, UKM, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Aini Hussain

Authors
  1. Zaher Mundher Yaseen
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  2. Ahmed El-Shafie
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  3. Haitham Abdulmohsin Afan
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  4. Mohammed Hameed
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  5. Wan Hanna Melini Wan Mohtar
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  6. Aini Hussain
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Correspondence to Zaher Mundher Yaseen.

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Yaseen, Z.M., El-Shafie, A., Afan, H.A. et al. RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Comput & Applic 27, 1533–1542 (2016). https://doi.org/10.1007/s00521-015-1952-6

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  • DOI: https://doi.org/10.1007/s00521-015-1952-6

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