{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:16:20Z","timestamp":1769854580430,"version":"3.49.0"},"reference-count":42,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,10,20]]},"abstract":"<jats:p>Visibility forecasting in offshore areas faces the problems of low observational data and complex weather. This paper proposes an intelligent prediction method of offshore visibility based on temporal convolutional network (TCN) and transfer learning to solve the problem. First, preprocess the visibility data sets of the source and target domains to improve the quality of the data. Then, build a model based on temporal convolutional network and transfer learning (TCN_TL) to learn the visibility data of the source domain. Finally, after transferring the knowledge learned from a large amount of data in the source domain, the model learns the small data set in the target domain. After completing the training, the model data of the European Mid-Range Weather Forecast Center (ECMWF) meteorological field were selected to test the model performance. The method proposed in this paper has achieved relatively good results in the visibility forecast of Qiongzhou Strait. Taking Haikou Station in the spring and winter of 2018 as an example, the forecast error is significantly lower than that before the transfer learning, and the forecast score is increased by 0.11 within the 0-1\u2009km level and the 24\u2009h forecast period. Compared with the CUACE forecast results, the forecast error of TCN_TL is smaller than that of the former, and the TS score is improved by 0.16. The results show that under the condition of small data sets, transfer learning improves the prediction performance of the model, and TCN_TL performs better than other deep learning methods and CUACE.<\/jats:p>","DOI":"10.1155\/2020\/8882279","type":"journal-article","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T05:05:05Z","timestamp":1603256705000},"page":"1-12","source":"Crossref","is-referenced-by-count":8,"title":["Application of Offshore Visibility Forecast Based on Temporal Convolutional Network and Transfer Learning"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5066-4716","authenticated-orcid":true,"given":"Zhenyu","family":"Lu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China"},{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7246-6844","authenticated-orcid":true,"given":"Cheng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing, China"},{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingya","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Meteorological Observatory, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"5","key":"1","first-page":"855","article-title":"Atmospheric visibility study","volume":"39","author":"G. 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