A Neural Network with Physical Mechanism for Predicting Airport Aviation Noise

A Neural Network with Physical Mechanism for Predicting Airport Aviation Noise

Blog Article

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 Psyllium 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 Studio Equipment 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.