Open Access

ARTICLE

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Ligang Yuan^1,*, Jiazhi Jin¹, Yan Xu², Ningning Zhang³, Bing Zhang⁴

1 College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
2 School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford, MK43 0AL, United Kingdom
3 Travelsky Technology Limited, Beijing, 100010, China
4 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

* Corresponding Author: Ligang Yuan. Email:

Computer Systems Science and Engineering 2023, 44(2), 1171-1185. https://doi.org/10.32604/csse.2023.027221

Received 13 January 2022; Accepted 22 April 2022; Issue published 15 June 2022

Abstract

Weather 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.

---

Keywords

---