Terminal Area Meteorological Scenario Pattern Recognition based on SDAE
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.
国家科技期刊平台
NSTL
万方数据
维普
