Deep Learning-Based Prediction of Commercial Aircraft Noise: A CNN-Transformer Hybrid Model Versus Support Vector Regression and Multi-Layer Perceptron - Dursun, Omer Osman (Mdpi, 2025)
The rapid growth of the aviation industry and increasing air traffic demand more careful attention to environmental concerns. Among these, aircraft noise is considered one of the main sources of environmental noise, especially after land-based transportation. The World Health Organization highlights noise pollution as the second-most important environmental factor after air pollution, with serious consequences for public health. Long-term exposure to high noise levels has been linked to problems such as cardiovascular disease and sleep disruption. In response, ICAO has introduced stricter standards especially in Annex 16, Volume I requiring aircraft to meet tighter noise limits. This study focuses on estimating the noise levels of Airbus and Boeing aircraft during approach, lateral, and flyover phases. The models use parameters such as maximum take-off and landing weights, engine thrust, and bypass ratio. Three approaches are compared: Support vector regression (SVR), a classical machine learning method, multi-layer perceptron(MLP), and a CNN-Transformer hybrid model, which combines deep learning and attention-based techniques. Their predictive performances were evaluated using MSE, RMSE, MAE, MAPE, and R2. The CNN-Transformer showed better results in all metrics. At the flyover point, it reached an R2 of 0.981, compared to 0.898 for SVR and 0.919 for MLP. At the lateral point, its MAE dropped to 0.58, while SVR had 1.64 and MLP 1.17. The attention-based model found patterns that the traditional one missed. It gave better results in several cases. Apart from this, some technologies used to reduce noise may also help save fuel and increase energy efficiency. For example, engines with a high bypass ratio can lower both noise and emissions. These kinds of solutions connect performance with environmental benefits. These insights could be useful for those involved in airport planning, aircraft engine design, or regulatory planning.
Keywords: aircraft noise; thrust; support vector regression; multi-layer perceptron; CNN-transformer