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(Guangzhou)","award":["GML2019ZD0306"],"award-info":[{"award-number":["GML2019ZD0306"]}]},{"name":"Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["BX2021324"],"award-info":[{"award-number":["BX2021324"]}]},{"name":"National Postdoctoral Program of Innovative Talents","award":["42192564"],"award-info":[{"award-number":["42192564"]}]},{"name":"National Postdoctoral Program of Innovative Talents","award":["2019YFA0606701"],"award-info":[{"award-number":["2019YFA0606701"]}]},{"name":"National Postdoctoral Program of Innovative Talents","award":["XDB42000000"],"award-info":[{"award-number":["XDB42000000"]}]},{"name":"National Postdoctoral Program of Innovative Talents","award":["XDA20060502"],"award-info":[{"award-number":["XDA20060502"]}]},{"name":"National Postdoctoral Program of Innovative Talents","award":["GML2019ZD0306"],"award-info":[{"award-number":["GML2019ZD0306"]}]},{"name":"National Postdoctoral Program of Innovative Talents","award":["BX2021324"],"award-info":[{"award-number":["BX2021324"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Typhoons can cause massive casualties and economic damage, and accurately predicting typhoon intensity has always been a hot topic both in theory and practice. In consideration with the spatial and temporal complexity of typhoons, machine learning methods have recently been applied in typhoon forecasting. In this paper, we attempt to improve typhoon intensity forecasting by treating it as a spatio-temporal problem in the deep learning field. In particular, we propose a novel typhoon intensity forecasting model named the Typhoon Intensity Spatio-temporal Prediction Network (TITP-Net). The proposed model takes multidimensional environmental variables and physical factors of typhoons into account and fully extracts the information from the datasets by capturing spatio-temporal dependencies with a spatial attention module, which includes two-dimensional and three-dimensional convolutional operations. A series of experiments with a comprehensive framework by using TITP-Net are conducted. The MAEs of the forecasts with 18, 24, 36 and 48 h lead time obtain a significant improvement by 7.02%, 6.53%, 6.25% and 5.37% compared with some existing deep learning models and dynamical models from official agencies.<\/jats:p>","DOI":"10.3390\/rs14205205","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:58:51Z","timestamp":1666141131000},"page":"5205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Improvement of Typhoon Intensity Forecasting by Using a Novel Spatio-Temporal Deep Learning Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Shuailong","family":"Jiang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanjie","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7611-0308","authenticated-orcid":false,"given":"Chunzai","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 510301, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1038\/nature03906","article-title":"Increasing destructiveness of tropical cyclones over the past 30 years","volume":"436","author":"Emanuel","year":"2005","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1080\/02255189.2017.1308316","article-title":"Natural disasters and land grabs:The politics of their intersection in the Philippines following super typhoon Haiyan","volume":"38","author":"Uson","year":"2017","journal-title":"Can. 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