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Prediction of Water Quality Parameters Using ANFIS Optimized by Intelligence Algorithms (Case Study: Gorganrood River)

  • Environmental Engineering
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Abstract

Water quality management and control has high importance in planning and developing of water resources. This study investigated application of Genetic Algorithm (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolution (DE) in improving the performance of adaptive neuro-fuzzy inference system (ANFIS), for evaluating the quality parameters of Gorganroud River water, such as Electrical Conductivity (EC), Sodium Absorption Ratio (SAR) and Total Hardness (TH). Accordingly, initially most suitable inputs were estimated for every model using sensitivity analysis and then all of the quality parameters were predicted using mentioned models. Investigations showed that for predicting EC and TH in test stage, ANFIS-DE with R2 values of 0.98 and 0.97, respectively and RMSE values of 73.03 and 49.55 and also MAPE values of 5.16 and 9.55, respectively were the most appropriate models. Also, ANFIS-DE and ANFIS-GA models had the best performance in prediction of SAR (R2 = 0.95, 0.91; RMSE = 0.43, 0.37 and MAPE = 13.43, 13.72) in test stage. It is noteworthy that ANFIS showed the best performance in prediction of all mentioned water quality parameters in training stage. The results indicated the ability of mentioned algorithms in improving the accuracy of ANFIS for predicting the quality parameters of river water.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Semnan University, Semnan, Iran

    Armin Azad, Hojat Karami, Saeed Farzin, Amir Saeedian & Fatemeh Sayyahi

  2. Water and Soil Faculty, Shahrood University of Technology, Semnan, Iran

    Hamed Kashi

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  1. Armin Azad
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  2. Hojat Karami
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  3. Saeed Farzin
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  4. Amir Saeedian
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  5. Hamed Kashi
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  6. Fatemeh Sayyahi
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Correspondence to Fatemeh Sayyahi.

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Azad, A., Karami, H., Farzin, S. et al. Prediction of Water Quality Parameters Using ANFIS Optimized by Intelligence Algorithms (Case Study: Gorganrood River). KSCE J Civ Eng 22, 2206–2213 (2018). https://doi.org/10.1007/s12205-017-1703-6

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  • DOI: https://doi.org/10.1007/s12205-017-1703-6

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