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Wyszukujesz frazę "artificial pollution" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Application of Artificial Neural Networks for Prediction of Air Pollution Levels in Environmental Monitoring
Autorzy:
Pawul, M.
Śliwka, M.
Powiązania:
https://bibliotekanauki.pl/articles/124279.pdf
Data publikacji:
2016
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
environmental monitoring
air pollution
artificial neural networks
prediction
Opis:
Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.
Źródło:
Journal of Ecological Engineering; 2016, 17, 4; 190-196
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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
    Wyświetlanie 1-2 z 2

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