基于SDAE的终端区气象场景模式识别方法  

Terminal Area Meteorological Scenario Pattern Recognition based on SDAE

作　　者：杨新湦[1] 罗秋晴 张召悦[1] YANG Xinsheng;LUO Qiuqing;ZHANG Zhaoyue(China College of Air Traffic Management,Civil Aviation University of China,Tianjin 3003000,China)

机构地区：[1]中国民航大学空中交通管理学院,天津300300

出　　处：《河南科技大学学报（自然科学版）》2024年第2期96-104,M0008,共10页Journal of Henan University of Science And Technology:Natural Science

基　　金：国家自然科学基金青年科学基金项目(71801215old);国家重点研发计划项目(KJZ25420200012)。

摘　　要：气象条件是影响终端区航空器运行安全及效率的主要因素之一。为提高终端区气象场景模式识别精度,采用基于堆叠降噪自编码(SDAE)的聚类模型,在输入层添加随机噪声、构建3层自编码、逐层贪婪训练,降维后的特征作为聚类的输入,实现气象场景的模式识别。以天津滨海国际机场2022年气象观测数据为例,基于SDAE与欧氏距离、汉明距离、曼哈顿距离等传统相似性距离度量方法,分别使用K-medoids与FCM两种聚类方法进行验证。结果表明:基于SDAE的相似性度量在K-medoids与FCM聚类中均表现最优,与其他相似性度量相比差异率分别达到22.4%,12%,17.7%与24.8%,10.7%,11.8%,且运算时间最短,证明了基于SDAE的度量、聚类效果最优,最终识别出8个气象场景,各场景分类清晰明确。To improve the accuracy of terminal area meteorological scene pattern recognition,this study adopts a clustering model based on Stacked Denoising Autoencoder.Noise is added to the input layer,and a three-layer autoencoder is constructed for greedy layer-wise training.The reduced-dimensional features are used as inputs for clustering to achieve meteorological scene pattern recognition.The method is validated using one year of meteorological data from Tianjin Binhai International Airport.Traditional similarity distance measures such as Euclidean distance,Hamming distance,and Manhattan distance are used with both K-medoids and FCM clustering methods.The results show that the similarity measure based on SDAE performs the best in both K-medoids and FCM clustering,with a difference rate of 22.4%,12%,17.7%,and 24.8%,10.7%,11.8%compared to other similarity measures,respectively.It also has the shortest computation time,demonstrating that the SDAE-based measure and clustering achieve the best performance.Ultimately,eight meteorological scenes are identified with clear and distinct classifications.

关 键 词：气象特征 堆叠降噪自编码 K-medoids FCM 

分 类 号：V355[航空宇航科学与技术—人机与环境工程]