A model based monitoring system for aircraft noise

Abstract

A new approach for an airport noise monitoring system is presented that comprises not only a number of measuring stations, but also a dedicated sound propagation model and an aircraft noise emission model. This approach enables estimation of noise levels in the whole area around the airport and not only at the location of the measuring stations. An additional advantage is that the locations of the measuring stations can be chosen more freely. Traditionally the measuring stations must be located near residential areas where the influence of other noise sources (such as cars or wind) cannot be eliminated. Better locations can be found that yield more reliable data and therefore more reliable noise levels. To update the sound propagation model frequently, the measuring stations not only measure noise levels, but also other parameters, like temperature and wind profile. The sound emission of the aircraft is derived from measurement results, complemented with a directional aircraft noise emission model. A tool was developed to visualize the power of combining data and models in this model-based monitoring system. The use of models opens possibilities for interesting applications such as i) short term noise forecasts, ii) scenario studies with various aircraft distributions and iii) enforcement of noise limits.

Investigation on Machine Learning Approaches for Environmental Noise Classifications

“…Several factors are considered in predicting ambient noise, including historical and real-time monitoring data. Van Den Berg et al [10] found that integrating the rules or patterns extracted from monitoring data with the acoustic theoretical calculation model improves the prediction accuracy of noise signifcantly. To better represent regional noise levels and increase data-gathering efciency, certain sampling procedures have been proposed while considering the concerns of conserving resources.…”

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

“…IoT must overcome some challenges to extract new insights from data [49]. In previous studies [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], environmental noise prediction mainly focused on the spatial propagation of noise, and there were few studies focused on the variation of noise in short-term. The noise data set used in this study is in seconds, which is more random.…”

“…The prediction of environmental noise heavily depends on historical and real-time noise monitoring data. The research by Frank et al [30] shows that combining the rules or patterns mined in the monitoring data with the acoustic theoretical calculation model can effectively improve the prediction accuracy of noise. In terms of reflecting regional noise levels, sometimes sampling strategies are put forward based on considerations of saving resources and improving data acquisition efficiency [31].…”