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

作     者：Armin Azad Hojat Karami Saeed Farzin Sayed-Farhad Mousavi Ozgur Kisi 

作者机构：Faculty of Civil EngineeringSemnan UniversitySemnan 35131-19111Iran Faculty of Natural Sciences and EngineeringIlia State UniversityTbilisi 0162Georgia 

出 版 物：《Water Science and Engineering》 (水科学与水工程（英文版）)

年 卷 期：2019年第12卷第1期

页      面：45-54页

核心收录：

学科分类：08[工学] 0815[工学-水利工程] 

主　　题：Water quality parameters ANFIS Evolutionary algorithm Particle swarm optimization Ant colony optimization for continuous domains 

摘      要：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.