A Neural Network with Physical Mechanism for Predicting Airport Aviation Noise

Abstract

Airport noise prediction models are divided into physics-guided methods and data-driven methods. The prediction results of physics-guided methods are relatively stable, but their overall prediction accuracy is lower than that of data-driven methods. However, machine learning methods have a relatively high prediction accuracy, but their prediction stability is inferior to physics-guided methods. Therefore, this article integrates the ECAC model, driven by aerodynamics and acoustics principles under the framework of deep neural networks, and establishes a physically guided neural network noise prediction model. This model inherits the stability of physics-guided methods and the high accuracy of data-driven methods. The proposed model outperformed physics-driven and data-driven models regarding prediction accuracy and generalization ability, achieving an average absolute error of 0.98 dBA in predicting the sound exposure level. This success was due to the fusion of physics-based principles with data-driven approaches, providing a more comprehensive understanding of aviation noise prediction.