In this paper an attempt has been made to predict and evaluate the aircraft-induced noise using model developed by means of machine learning. First step in the development of the model was to artificially calculate noise caused by aircrafts on several locations in vicinity of the airport. To investigate the appropriateness of this approach, the prediction of the developed model was also compared with the most widely used aircraft noise modelling software INM, developed by Federal Aviation Authority.
In this research, an artificial neural network was proposed to predict aircraft-produced noise. The
precision of the developed model was evaluated using criteria such as the Mean square error (MSE), goodness of fit (R-square) and the Mean absolute error (MAE). Initial idea for developing this model was to propose easy to use noise estimation procedure for airport operators. This procedure would be free of the detailed modelling necessary when using currently available commercial software ...packages.
TY - CONF
AU - Lukić, Miloš
AU - Gavran, Dejan
AU - Fric, Sanja
AU - Ilić, Vladan
AU - Vranjevac, Stefan
AU - Trpčevski, Filip
PY - 2021
UR - https://grafar.grf.bg.ac.rs/handle/123456789/2434
AB - In this paper an attempt has been made to predict and evaluate the aircraft-induced noise using model developed by means of machine learning. First step in the development of the model was to artificially calculate noise caused by aircrafts on several locations in vicinity of the airport. To investigate the appropriateness of this approach, the prediction of the developed model was also compared with the most widely used aircraft noise modelling software INM, developed by Federal Aviation Authority.
In this research, an artificial neural network was proposed to predict aircraft-produced noise. The
precision of the developed model was evaluated using criteria such as the Mean square error (MSE), goodness of fit (R-square) and the Mean absolute error (MAE). Initial idea for developing this model was to propose easy to use noise estimation procedure for airport operators. This procedure would be free of the detailed modelling necessary when using currently available commercial software packages.
PB - Silesian University Press, Gliwice, Poland
T1 - Prediction of aircraft noise using machine learning
UR - https://hdl.handle.net/21.15107/rcub_grafar_2434
ER -
@conference{
author = "Lukić, Miloš and Gavran, Dejan and Fric, Sanja and Ilić, Vladan and Vranjevac, Stefan and Trpčevski, Filip",
year = "2021",
abstract = "In this paper an attempt has been made to predict and evaluate the aircraft-induced noise using model developed by means of machine learning. First step in the development of the model was to artificially calculate noise caused by aircrafts on several locations in vicinity of the airport. To investigate the appropriateness of this approach, the prediction of the developed model was also compared with the most widely used aircraft noise modelling software INM, developed by Federal Aviation Authority.
In this research, an artificial neural network was proposed to predict aircraft-produced noise. The
precision of the developed model was evaluated using criteria such as the Mean square error (MSE), goodness of fit (R-square) and the Mean absolute error (MAE). Initial idea for developing this model was to propose easy to use noise estimation procedure for airport operators. This procedure would be free of the detailed modelling necessary when using currently available commercial software packages.",
publisher = "Silesian University Press, Gliwice, Poland",
title = "Prediction of aircraft noise using machine learning",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2434"
}
Lukić, M., Gavran, D., Fric, S., Ilić, V., Vranjevac, S.,& Trpčevski, F.. (2021). Prediction of aircraft noise using machine learning.
Silesian University Press, Gliwice, Poland..
https://hdl.handle.net/21.15107/rcub_grafar_2434
Lukić M, Gavran D, Fric S, Ilić V, Vranjevac S, Trpčevski F. Prediction of aircraft noise using machine learning. 2021;.
https://hdl.handle.net/21.15107/rcub_grafar_2434 .
