Aircraft Noise Prediction Based on Machine Learning Model

doi:10.16356/j.1005-1120.2023.S2.008

Transactions of Nanjing University of Aeronautics & Astronautics

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Aircraft Noise Prediction Based on Machine Learning Model

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Abstract:

In order to explore the aircraft noise prediction methods beyond the best practice model and scientific model， this paper uses multiple linear regression model and random forest regression model to predict the aircraft noise value of Seattle-Tacoma International Airport in the summer of 2020—2022. The experiment confirm the feasibility and advantages of the machine learning model in aircraft noise prediction tasks and find that the mean R2 predicted by the random forest regression model is 74.469%， 5.361% higher than that of the multiple linear regression model. The mean RMSE predicted by the random forest regression model is 0.814， 0.106 lower than that of the multiple linear regression model.

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This work was supported by the National Natural Science Foundation of China （No.52202442） and National Key R&D Program of China （No.2022YFB260-2403）.

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