Water Science and Engineering 2019, 12(1) 45-54 DOI:   https://doi.org/10.1016/j.wse.2018.11.001  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
Water quality parameters
ANFIS
Evolutionary algorithm
Particle swarm optimization
Ant colony optimization for continuous domains
Authors
PubMed

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

Armin Azad a,Hojat Karami a,*,  Saeed Farzin a, Sayed-Farhad Mousavia, Ozgur Kisib

a Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran
b Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi 0162, Georgia

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.

Keywords Water quality parameters   ANFIS   Evolutionary algorithm   Particle swarm optimization   Ant colony optimization for continuous domains  
Received 2018-05-18 Revised 2019-01-03 Online: 2019-03-31 
DOI: https://doi.org/10.1016/j.wse.2018.11.001
Fund:
Corresponding Authors: Hojat Karami
Email: hkarami@semnan.ac.ir
About author:

References:

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