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机场范围内的天气状况对机场运行有着较为显著的影响.然而,天气在发生时间、影响范围等方面的随机性给机场容量评估和流量调配带来一定的困难.运用SOM网络对2014年上海浦东国际机场的天气进行聚类分析,并运用DB指标和Silhouette指标对聚类结果进行评价.结果表明:SOM网络在机场天气聚类上有很好的精度与鲁棒性,2014年浦东机场的天气可按影响程度分为典型的五类,即:无影响、影响小、影响极大、影响大、影响中等.为实现航空天气聚类分析及空中交通流量管理自动化提供了有益参考.
However, the randomness of the weather in the time of occurrence and the influence scope brings some difficulties to the airport capacity assessment and traffic allocation.Using the SOM network, the influence of the weather conditions on the airport operation in Shanghai Pudong International Airport weather clustering analysis, and use the DB indicators and Silhouette indicators to evaluate the clustering results.The results show that: SOM network in airport weather clustering has good accuracy and robustness, weather in 2014 Pudong Airport According to the degree of influence, it can be divided into five typical categories: no impact, small impact, great impact, large impact and medium impact, which provides a useful reference for the realization of the clustering analysis of aeronautical weather and the automation of air traffic flow management.