Modelling aircraft noise map around an airport using machine learning

The measurement of aircraft noise is very important for the industry of air transportation, for residents and for the municipality.In the United States of America, the Federal Aviation Authority (FAA) has a managing tool for aircraft noise map called Aeronautical Environmental Design Tool (AEDT) (Boeker Fleming, 2008); in French Bruitparif is the tool which satisfies the European directive for noise map elaboration strategy. The noise map enables city authorities, residents, and the airport manager to control the noise produced by aeronautical activities in order to master, for instance, the building of infrastructures surrounding the airport zone - a dataset built with a relational data base management system obtained from secondary radar detection. These data, after having gone through a data cleaning process, will be brought and tested with functions such as Naive Bayes, decision tree and random forest. Based on confusion matrix as a metric, we assessed the provided result by the decision tree with metrics as “ 98% for precision, 97% for recall and 97% for f1-score”. This article emphasizes that on the one hand, the data used for machine learning have the same quality in terms of integrity and precision of the radar itself and on the other hand the performance resides on the confusion matrix.