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标题：Modeling Pollution Index Using Artificial Neural Network and Multiple Linear Regression Coupled with Genetic Algorithm
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作者：Iman Ali Abdulkareem ; Abdulhussain A. Abbas ; Ammar Salman Dawood 等
期刊名称：Inżynieria Ekologiczna
印刷版ISSN：2081-139X
电子版ISSN：2392-0629
出版年度：2022
卷号：23
期号：3
页码：236-250
DOI：10.12911/22998993/146177
语种：English
出版社：Polish Society of Ecological Engineering (PTIE)
摘要：Shatt Al-Arab River in Basrah province, Iraq, was assessed by applying comprehensive pollution index (CPI) at fifteen sampling locations from 2011 to 2020, taking into consideration twelve physicochemical parameters which included pH, Tur., TDS, EC, TH, Na+, K+, Ca+2, Mg+2, Alk., SO4-2, and Cl-. The effectiveness of multiple linear regression (MLR) and artificial neural network (ANN) for predicting comprehensive pollution index was examined in this research. In order to determine the ideal values of the predictor parameters that lead to the lowest CPI value, the genetic algorithm coupled with multiple linear regression (GA-MLR) was used. A multi-layer feed-forward neural network with backpropagation algorithm was used in this study. The optimal ANN structure utilized in this research consisted of three layers: the input layer, one hidden layer, and one output layer. The predicted equation of the comprehensive pollution index was created using the regression technique and used as an objective function of the genetic algorithm. The minimum predicted comprehensive pollution index value recommended by the GA-MLR approach was 0.3777.
关键词：Shatt Al-Arab River ;comprehensive pollution index ;multiple linear regression ;artificial neural network ;genetic algorithm