{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T21:20:23Z","timestamp":1787779223736,"version":"build-2784847793"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0608002"],"award-info":[{"award-number":["2020YFA0608002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42075141"],"award-info":[{"award-number":["42075141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["22120190207"],"award-info":[{"award-number":["22120190207"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>A typhoon is an extreme weather event with strong destructive force, which can bring huge losses of life and economic damage to people. Thus, it is meaningful to reduce the prediction errors of typhoon intensity forecasting. Artificial and deep neural networks have recently become widely used for typhoon forecasting in order to ensure typhoon intensity forecasting is accurate and timely. Typhoon intensity forecasting models based on long short-term memory (LSTM) are proposed herein, which forecast typhoon intensity as a time series problem based on historical typhoon data. First, the typhoon intensity forecasting models are trained and tested with processed typhoon data from 2000 to 2014 to find the optimal prediction factors. Then, the models are validated using the optimal prediction factors compared to a feed-forward neural network (FNN). As per the results of the model applied for typhoons Chan-hom and Soudelor in 2015, the model based on LSTM using the optimal prediction factors shows the best performance and lowest prediction errors. Thus, the model based on LSTM is practical and meaningful for predicting typhoon intensity within 120 h.<\/jats:p>","DOI":"10.3390\/a14030083","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:39:07Z","timestamp":1614904747000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8102-3137","authenticated-orcid":false,"given":"Shijin","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wansuo","family":"Duan","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s00376-018-8001-1","article-title":"Ensemble Forecastings of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations","volume":"36","author":"Huo","year":"2019","journal-title":"Adv. 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