Study on recognition method of similar weather scenes in terminal area

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dc.contributor.authorYuan, Ligang
dc.contributor.authorJin, Jiazhi
dc.contributor.authorXu, Yan
dc.contributor.authorZhang, Ningning
dc.contributor.authorZhang, Bing
dc.date.accessioned2022-07-28T10:12:41Z
dc.date.available2022-07-28T10:12:41Z
dc.date.issued2022-06-15
dc.description.abstractWeather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.en_UK
dc.identifier.citationYuan L, Jin J, Xu Y, et al., (2022) Study on recognition method of similar weather scenes in terminal area. Computer Systems Science and Engineering, Volume 44, Issue 2, July 2022, pp. 1171-1185en_UK
dc.identifier.issn0267-6192
dc.identifier.urihttps://doi.org/10.32604/csse.2023.027221
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18242
dc.language.isoenen_UK
dc.publisherTech Science Pressen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAir trafficen_UK
dc.subjectterminal areaen_UK
dc.subjectsimilar scenesen_UK
dc.subjectdeep embedding clusteringen_UK
dc.titleStudy on recognition method of similar weather scenes in terminal areaen_UK
dc.typeArticleen_UK

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