- Tytuł:
- 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 - Powiązania:
- https://bibliotekanauki.pl/articles/2068477.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polskie Towarzystwo Inżynierii Ekologicznej
- Tematy:
-
Shatt Al-Arab river
comprehensive pollution index
multiple linear regression
artificial neural network
genetic algorithm - Opis:
- 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.
- Źródło:
-
Journal of Ecological Engineering; 2022, 23, 3; 236--250
2299-8993 - Pojawia się w:
- Journal of Ecological Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł