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Improved Water Quality Prediction with Hybrid Wavelet-Genetic Programming Model and Shannon Entropy

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Abstract

Prediction of biochemical oxygen demand (BOD) as the main pollution indicators of organic pollution in freshwater resources is necessary. In the present work, a hybrid wavelet-genetic programming (WGP) method was implemented to improve prediction of BOD. The Shannon entropy was used to identify the optimal input combinations of WGP. In addition, an investigation was done to find which functions of wavelet and decomposition levels have better results in conjunction with genetic programming (GP). For comparison of WGP efficiency, five machine learning methods consisting of WANN (wavelet-artificial neural network), ANN (artificial neural network), GP, DT (decision tree) and BN (Bayesian network) were considered. Experiments on wavelet-processed data revealed that the best results were obtained when the models WGP and WANN were calibrated at three levels of decomposition using the Dmey mother wavelet function. The WGP model created rational forecasts for the peak BOD values. The results show that the use of Shannon entropy is suitable for determining the optimal composition of inputs to machine learning methods. Comparison of the results indicate that the WGP model is superior to the GP, ANN, DT, BN and WANN models based on data from the Varian Hotel and Dam Input stations.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Qom, Qom, Iran

    Hamideh Jafari & Taher Rajaee

  2. Department of Civil Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

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  1. Hamideh Jafari
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  2. Taher Rajaee
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  3. Ozgur Kisi
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Correspondence to Ozgur Kisi.

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Jafari, H., Rajaee, T. & Kisi, O. Improved Water Quality Prediction with Hybrid Wavelet-Genetic Programming Model and Shannon Entropy. Nat Resour Res 29, 3819–3840 (2020). https://doi.org/10.1007/s11053-020-09702-7

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  • DOI: https://doi.org/10.1007/s11053-020-09702-7

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