Modeling Pollution Index Using Artificial Neural Network and Multiple Linear Regression Coupled with Genetic Algorithm

• Autorzy: Abdulkareem Iman Ali , Abbas Abdulhussain A. , Dawood Ammar Salman

Identyfikatory

DOI

10.12911/22998993/146177

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Shatt Al-Arab river; comprehensive pollution index; multiple linear regression; artificial neural network; genetic algorithm

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2022
• Tom: Vol. 23, nr 3
• Strony: 236--250
• Opis fizyczny: Bibliogr. 49 poz., rys., tab.

Twórcy

autor

Abdulkareem Iman Ali

• Civil Engineering Department, College of Engineering, University of Basrah, Karmat Ali, Basra, Basra Governorate, 61004, Iraq

autor

Abbas Abdulhussain A.

• Civil Engineering Department, College of Engineering, University of Basrah, Karmat Ali, Basra, Basra Governorate, 61004, Iraq

• https://orcid.org/0000-0003-1759-3984

autor

Dawood Ammar Salman

• Civil Engineering Department, College of Engineering, University of Basrah, Karmat Ali, Basra, Basra Governorate, 61004, Iraq

• https://orcid.org/0000-0003-1210-6673

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