关于多跑道机场噪声优化预测方法

Optimization of Multi-Runway Airports Noise Prediction

多跑道机场噪声预测是机场规划和改扩建的重要基础,随着机场跑道数量的增加,航班、跑道、飞行程序之间组合更加多样化,机场噪声预测问题更加复杂。为了得到可靠性高的预测结果,根据不同跑道的航迹聚类和机型聚类,把聚类结果的每簇中心航迹和代表机型数据组合导入INM(Intergrated Noise Models)计算噪声值构成噪声数据库,通过贝叶斯分类算法构建了一个采用贝叶斯分类的多跑道机场噪声预测模型。输入航班号、机型、航迹、目的地、出港点等基础数据即可快速确定航迹、机型所属类别和跑道号,然后查询噪声数据库得到噪声预测结果。实验结果表明,上述模型能够在一定误差范围内方便快捷地预测出机场周围敏感点的噪声,从而验证了预测模型的合理性和有效性。

Multi - runway airports noise prediction is the important basis of airport plan and expansion. With the increase in the number of airport runway, the combinations of flights, runways and flight procedures are more diverse and the noise prediction of multi - runway airports is more complexly. Therefore, in order to obtain the high reliabili- ty prediction results, this paper focused on track clustering and aircraft type clustering. And then the data of the center track of each cluster and the representative models of each cluster were input into Integrated Noise Models （INM） to calculate the noise value and constitute the noise database. A multi -runway airport noise prediction model based on Bayesian classification was proposed with Bayesian classification algorithm. Once the data of flight number, air- craft type, track, destination and departure point are input into the model, the track, aircraft type and the coder of runway can be quickly determined. Then the noise prediction results can be obtained by querying the noise database. Experimental results show that the proposed model can be used to predict the noise of the sensitive points around the airport within a certain error range. Therefore, the rationality and validity of the model are verified.