Volume 12 Issue 1
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Article Navigation >  Water Science and Engineering  > 2019  >  12(1): 45-54
Armin Azad, Hojat Karami, Saeed Farzin, Sayed-Farhad Mousavi, Ozgur Kisi. 2019: Modeling river water quality parameters using modified adaptive neuro fuzzy inference system. Water Science and Engineering, 12(1): 45-54. doi: 10.1016/j.wse.2018.11.001
Citation: Armin Azad, Hojat Karami, Saeed Farzin, Sayed-Farhad Mousavi, Ozgur Kisi. 2019: Modeling river water quality parameters using modified adaptive neuro fuzzy inference system. Water Science and Engineering, 12(1): 45-54. doi: 10.1016/j.wse.2018.11.001
shu

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

doi: 10.1016/j.wse.2018.11.001

  • a Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran

  • b Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi 0162, Georgia

More Information
  • Corresponding author: Hojat Karami
  • Received Date: 2018-05-18
  • Rev Recd Date: 2019-01-03
    Abstract
  • Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling
    water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the
    adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more
    suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being
    trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO)
    and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood
    River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and
    gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved
    solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using
    SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with
    ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.

     

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