With the increasingly severe problem of aircraft noise pollution around airports, it is urgent to explore accurate and effective aircraft noise assessment methods. This article proposes a physics-based PSO-BPNN model based on the European Civil Aviation Conference (ECAC) best practice model, backpropagation neural network (BPNN), and particle swarm optimization (PSO) to enrich the methodology system of aircraft noise assessment. The primary modeling process is as follows. Firstly, based on airport parameters, flight information, and other data, the theoretical noise levels of ground monitoring point are calculated using the ECAC dynamic model. Then, a dataset containing measured noise values, theoretical noise values, trajectory data, and meteorological data is constructed to train the physics-based BPNN model, in order to correct the theoretical noise level. Finally, the PSO algorithm is introduced to optimize the parameters of the BPNN model and the construction of the physics-based PSO-BPNN model is completed. By taking Hefei Xinqiao International Airport (HFE) as a research case, the experimental results show that the physics-based PSO-BPNN model, which combines best practice model and machine learning model, demonstrates better performance than the ECAC model and physics-based BPNN model because of its balance between stability and flexibility. In the validation set, the error of 74.77 % of the predicted results was within ±3 dB(A), and the coefficient of determination between all predicted values and measured values reached 0.9450, which indicates the physics-based PSO-BPNN model a potential aircraft noise assessment solution.

中文翻译：

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用于民用飞机噪声评估的基于物理的 PSO-BPNN 模型

随着机场周边飞机噪声污染问题日益严峻，迫切需要探索准确有效的飞机噪声评估方法。本文在欧洲民航会议（ECAC）最佳实践模型、反向传播神经网络（BPNN）和粒子群优化（PSO）的基础上提出了一种基于物理的PSO-BPNN模型，以丰富飞机噪声评估的方法体系。主要建模过程如下。首先，根据机场参数、航班信息等数据，利用ECAC动态模型计算出地面监测点的理论噪声水平。然后，构建包含测量噪声值、理论噪声值、轨迹数据和气象数据的数据集来训练基于物理的BPNN模型，以校正理论噪声水平。最后引入PSO算法对BPNN模型参数进行优化，完成基于物理的PSO-BPNN模型的构建。以合肥新桥国际机场（HFE）为研究案例，实验结果表明，结合最佳实践模型和机器学习模型的基于物理的PSO-BPNN模型比ECAC模型和基于物理的BPNN表现出更好的性能模型因其在稳定性和灵活性之间的平衡而被采用。在验证集中，74.77%的预测结果误差在±3 dB(A)以内，所有预测值与测量值之间的决定系数达到0.9450，这表明基于物理的PSO-BPNN模型具有潜在的应用价值。飞机噪声评估解决方案。