Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

• Autorzy: Mansourkhaki A. , Berangi M. , Haghiri M.

Identyfikatory

DOI

10.12911/22998993/79411

• Języki publikacji: EN

Abstrakty

EN

Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effects on humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to find new approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LAeq) by measuring the traffic volume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficient of determination (R2) and the Mean Squared Error (MSE). Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

Słowa kluczowe

EN

MLP; RBF; neural network; noise prediction; road traffic noise

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2018
• Tom: Vol. 19, nr 1
• Strony: 113--121
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Mansourkhaki A.

• School of Civil Engineering, Iran University of Science & Technology, Narmak, Tehran, Iran

autor

Berangi M.

• School of Civil Engineering, Iran University of Science & Technology, Narmak, Tehran, Iran

autor

Haghiri M.

• School of Civil Engineering, Iran University of Science & Technology, Narmak, Tehran, Iran

Bibliografia

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Uwagi

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