Prediction of industrial pollution by radial basis function networks

• Autorzy: Djebbri N. , Rouainia M.

Identyfikatory

DOI

10.5277/epe180311

• Języki publikacji: EN

Abstrakty

EN

Atmospheric pollution has been receiving a significant interest for several decades since industries cause more and more pollution. Thanks to the development of many prediction techniques, scientists and industries are focusing more on pollution prediction. The aim of this work is to predict the two pollutant concentrations (NO_x and CO) in industrial sites by a modified radial basis function (RBF) based neural network. The modification considered the spread parameter h of the activation function in the RBF network. In order to get the best network, the variations of this parameter for three cases were considered. In the first case, only pollutants concentrations variables were used, while in the second one, only the meteorological variables were utilized. In the third case, pollutants' concentrations were connected with meteorological variables. Based on calculation errors, the best model that ensures the best monitoring of pollutants concentration could be identified.

Słowa kluczowe

EN

forecasting; RBF; artificial neural network; pollution

PL

prognozowanie; RBF; sztuczna sieć neuronowa; zanieczyszczenie

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Environment Protection Engineering
• Rocznik: 2018
• Tom: Vol. 44, nr 3
• Strony: 153--164
• Opis fizyczny: Bibliogr. 19 poz., tab., rys.

Twórcy

autor

Djebbri N.

• University 20 août, 1955 Skikda, Algeria

autor

Rouainia M.

• University 20 août, 1955 Skikda, Algeria

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).