Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems
Faster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction (ITR) detection and classification, since those problems usually lead to path identification biases as well as...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/4354198 |
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| _version_ | 1832567720343764992 |
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| author | Ming Yang Hao Ma Bomin Chen Guangtao Dong |
| author_facet | Ming Yang Hao Ma Bomin Chen Guangtao Dong |
| author_sort | Ming Yang |
| collection | DOAJ |
| description | Faster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction (ITR) detection and classification, since those problems usually lead to path identification biases as well as low accuracy and generalization ability of recognition algorithm. In this paper, an improved recognition method of national ITR (NITR) path in China based on faster R-CNN in complicated meteorological systems is proposed. Firstly, quality control of the original dataset of strong cooling processes is carried out by means of data filtering. Then, according to the NITR standard and the characteristics of NITR, the NITR dataset in China is established by the intensive temperature-reduction areas located through spatial transformation. Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine (SVM) is used for path classification to enhance the confidence of classification. Finally, the improved faster R-CNN model is used to identify, classify, and locate the path of NITR events. The experimental results show that, compared to other models, the improved faster R-CNN algorithm greatly improves the performance of NITR’s path recognition, especially for the mixed NITR paths and single NITR paths. Therefore, the improved faster R-CNN model has fast calculation speed, high recognition accuracy, good robustness, and generalization ability of NITR path recognition. |
| format | Article |
| id | doaj-art-3f585eb26f164d3dbb583a7f1034e6e1 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3f585eb26f164d3dbb583a7f1034e6e12025-02-03T01:00:46ZengWileyComplexity1099-05262022-01-01202210.1155/2022/4354198Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological SystemsMing Yang0Hao Ma1Bomin Chen2Guangtao Dong3Zhejiang Meteorological Information and Network CenterZhejiang Climate CenterShanghai Climate CenterShanghai Climate CenterFaster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction (ITR) detection and classification, since those problems usually lead to path identification biases as well as low accuracy and generalization ability of recognition algorithm. In this paper, an improved recognition method of national ITR (NITR) path in China based on faster R-CNN in complicated meteorological systems is proposed. Firstly, quality control of the original dataset of strong cooling processes is carried out by means of data filtering. Then, according to the NITR standard and the characteristics of NITR, the NITR dataset in China is established by the intensive temperature-reduction areas located through spatial transformation. Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine (SVM) is used for path classification to enhance the confidence of classification. Finally, the improved faster R-CNN model is used to identify, classify, and locate the path of NITR events. The experimental results show that, compared to other models, the improved faster R-CNN algorithm greatly improves the performance of NITR’s path recognition, especially for the mixed NITR paths and single NITR paths. Therefore, the improved faster R-CNN model has fast calculation speed, high recognition accuracy, good robustness, and generalization ability of NITR path recognition.http://dx.doi.org/10.1155/2022/4354198 |
| spellingShingle | Ming Yang Hao Ma Bomin Chen Guangtao Dong Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems Complexity |
| title | Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems |
| title_full | Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems |
| title_fullStr | Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems |
| title_full_unstemmed | Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems |
| title_short | Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems |
| title_sort | intensive cold air invasion detection and classification with deep learning in complicated meteorological systems |
| url | http://dx.doi.org/10.1155/2022/4354198 |
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