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Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

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Abstract

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R 2=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA–ANN (R 2=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.

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Acknowledgement

The authors would like to express their appreciation to Ms. Maya Binder for final editing of the English text.

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Authors and Affiliations

  1. Department of Civil Engineering, Razi University, Kermanshah, Iran

    Hamid Moeeni, Hossein Bonakdari & Isa Ebtehaj

Authors
  1. Hamid Moeeni
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  2. Hossein Bonakdari
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  3. Isa Ebtehaj
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Corresponding author

Correspondence to Hossein Bonakdari.

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Corresponding editor: Rajib Maity

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Moeeni, H., Bonakdari, H. & Ebtehaj, I. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. J Earth Syst Sci 126, 18 (2017). https://doi.org/10.1007/s12040-017-0798-y

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  • DOI: https://doi.org/10.1007/s12040-017-0798-y

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