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Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation

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Abstract

An accurate estimation of flow using different models is an issue for water resource researchers. In this study, support vector regression (SVR) and gene expression programming (GEP) models in daily and monthly scale were used in order to simulate Gamasiyab River flow in Nahavand, Iran. The results showed that although the performance of models in daily scale was acceptable and the result of SVR model was a little better, their performance in the daily scale was really better than the monthly scale. Therefore, wavelet transform was used and the main signal of every input was decomposed. Then, by using principal component analysis method, important sub-signals were recognized and used as inputs for the SVR and GEP models to produce wavelet-support vector regression (WSVR) and wavelet-gene expression programming. The results showed that the performance of WSVR was better than the SVR in such a way that the combination of SVR with wavelet could improve the determination coefficient of the model up to 3% and 18% for daily and monthly scales, respectively. Totally, it can be said that the combination of wavelet with SVR is a suitable tool for the prediction of Gamasiyab River flow in both daily and monthly scales.

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Acknowledgements

This study has been supported by the minister of Regional Water of Hamedan by providing necessary data and information.

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Authors and Affiliations

  1. Department of Water Resources Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Abazar Solgi & Amir Pourhaghi

  2. Department of Science and Water Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Ramin Bahmani

  3. Department of Hydrology and Water Resources, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Heidar Zarei

Authors
  1. Abazar Solgi
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  2. Amir Pourhaghi
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  3. Ramin Bahmani
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  4. Heidar Zarei
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Correspondence to Abazar Solgi.

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Corresponding editor: Prashant K Srivastava

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Solgi, A., Pourhaghi, A., Bahmani, R. et al. Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. J Earth Syst Sci 126, 65 (2017). https://doi.org/10.1007/s12040-017-0850-y

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  • DOI: https://doi.org/10.1007/s12040-017-0850-y

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