{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:22:05Z","timestamp":1747196525147,"version":"3.40.5"},"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Start-up Fund Project of Heihe University","award":["2019-KYQDJJ-YJ08"],"award-info":[{"award-number":["2019-KYQDJJ-YJ08"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Typhoons are natural disasters characterized by their high frequency of occurrence and significant impact, often leading to secondary disasters. In this study, we propose a prediction model for the trend of typhoon disasters. Utilizing neural networks, we calculate the forgetting gate, update gate, and output gate to forecast typhoon intensity, position, and disaster trends. By employing the concept of big data, we collected typhoon data using Python technology and verified the model\u2019s performance. Overall, the model exhibited a good fit, particularly for strong tropical storms. However, improvements are needed to enhance the forecasting accuracy for tropical depressions, typhoons, and strong typhoons. The model demonstrated a small average error in predicting the latitude and longitude of the typhoon\u2019s center position, and the predicted path closely aligned with the actual trajectory.<\/jats:p>","DOI":"10.1371\/journal.pone.0299530","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T17:49:19Z","timestamp":1714067359000},"page":"e0299530","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Typhoon disaster emergency forecasting method based on big data"],"prefix":"10.1371","volume":"19","author":[{"given":"Hong","family":"Huo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqiu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7879-7899","authenticated-orcid":true,"given":"Shiying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"pone.0299530.ref001","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.tcrr.2022.04.003","article-title":"Characteristics of rainstorm in Fujian induced by typhoon passing through Taiwan Island[J]","volume":"11","author":"Siyu Yin","year":"2022","journal-title":"Tropical Cyclone Research and Review"},{"key":"pone.0299530.ref002","first-page":"1","article-title":"Typhoon strikes, distracted analyst and forecast accuracy: Evidence from China[J]","volume":"51","author":"Liu Na","year":"2023","journal-title":"Finance Research Letters"},{"key":"pone.0299530.ref003","first-page":"1","article-title":"Development of a coupled genetic algorithm and empirical typhoon wind model and its application[J]","volume":"248","author":"Gong Yijie","year":"2022","journal-title":"Ocean Engineering"},{"issue":"P3","key":"pone.0299530.ref004","first-page":"1","article-title":"Forecasting of typhoon wave based on hybrid machine learning models[J]","volume":"266","author":"Gong Yijie","year":"2022","journal-title":"Ocean Engineering"},{"issue":"3","key":"pone.0299530.ref005","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.nhres.2022.08.005","article-title":"Risk assessment of typhoon storm surge based on a simulated annealing algorithm and the least squares method: a case study in Guangdong Province, China[J]","volume":"2","author":"Guo Tengjiao","year":"2022","journal-title":"Natural Hazards Research"},{"key":"pone.0299530.ref006","first-page":"1","article-title":"Evaluating perception of community resilience to typhoon disasters in China based on grey relational TOPSIS model[J]","volume":"84","author":"Peng Yi","year":"2023","journal-title":"International Journal of Disaster Risk Reduction"},{"key":"pone.0299530.ref007","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.tcrr.2022.09.003","article-title":"Impact-based forecasting for improving the capacity of typhoon-related disaster risk reduction in typhoon committee region[J]","volume":"11","author":"Jixin Yu","year":"2022","journal-title":"Tropical Cyclone Research and Review"},{"key":"pone.0299530.ref008","first-page":"1","article-title":"Resilience enhancement of distribution network under typhoon disaster based on two-stage stochastic programming[J]","volume":"338","author":"Hui Hou","year":"2023","journal-title":"Applied Energy"},{"issue":"S16","key":"pone.0299530.ref009","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.egyr.2022.12.146","article-title":"Review of distribution network\u2019s resilience based on typhoon disaster[J]","volume":"8","author":"Chen Donghai","year":"2022","journal-title":"Energy Reports"},{"key":"pone.0299530.ref010","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.renene.2022.02.072","article-title":"Multi-stage typhoon-induced wind effects on offshore wind turbines using a data-driven wind speed field model[J]","volume":"188","author":"H. Wang","year":"2022","journal-title":"Renewable Energy"},{"key":"pone.0299530.ref011","first-page":"1","article-title":"disaster assessment of landfalling typhoons in different regions of China over 2004\u20132020[J","volume":"228","author":"Ren Hehe","year":"2022","journal-title":"Journal of Wind Engineering & Industrial Aerodynamics"},{"key":"pone.0299530.ref012","first-page":"1","article-title":"An improved typhoon monitoring model based on precipitation water vapor and pressure[J]","volume":"12","author":"Li Junyu","year":"2023","journal-title":"Geodesy and Geodynamics"},{"key":"pone.0299530.ref013","first-page":"1","article-title":"Building loss assessment using deep learning algorithm from typhoon Rusa[J]","volume":"10","author":"Ji-Myong Kim","year":"2024","journal-title":"Heliyon"},{"key":"pone.0299530.ref014","first-page":"1","article-title":"Influential factors of long-term and seasonal 137Cs change in agricultural and forested rivers: Temperature, water quality and an intense Typhoon Event[J]","volume":"338","author":"Hideki Tsuji","year":"2023","journal-title":"Environmental Pollution"},{"key":"pone.0299530.ref015","first-page":"1","article-title":"Optimal resilience enhancement dispatch of a power system with multiple offshore wind farms considering uncertain typhoon parameters[J]","volume":"153","author":"Liang Yutao","year":"2023","journal-title":"International Journal of Electrical Power and Energy Systems"},{"key":"pone.0299530.ref016","first-page":"1","article-title":"Storm hardening and insuring energy systems in typhoon-prone regions: A techno-economic analysis of hybrid renewable energy systems in the Philippines\u2019 Busuanga island cluster[J]","volume":"50","author":"Michael T. Castro","year":"2023","journal-title":"Energy Strategy Reviews"},{"key":"pone.0299530.ref017","first-page":"1","article-title":"Testing heavy rainfall change in Jeju Island, Korea and its linkage to typhoon activity over East Asia during the Holocene[J]","volume":"627","author":"Jaesoo Lim","year":"2023","journal-title":"Palaeogeography, Palaeoclimatology, Palaeoecology"},{"key":"pone.0299530.ref018","first-page":"1","article-title":"Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs[J]","volume":"1:","author":"Arun Kumar","year":"2024","journal-title":"Ain Shams Engineering Journal"},{"key":"pone.0299530.ref019","first-page":"1","article-title":"Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network[J]","volume":"45","author":"Josephine D. 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Vargil Vijay","year":"2023","journal-title":"Electronics and Energy"},{"key":"pone.0299530.ref022","first-page":"1","article-title":"Semi-supervised bidirectional RNN for misinformation detection[J]","volume":"10","author":"Xishuang Dong","year":"2022","journal-title":"Machine Learning with Applications"},{"key":"pone.0299530.ref023","first-page":"1","article-title":"Modelling a deep network using CNN and RNN for accident classification[J]","volume":"27","author":"V Raviteja Kanakala","year":"2023","journal-title":"Measurement: Sensors"},{"key":"pone.0299530.ref024","first-page":"1","article-title":"A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid[J]","volume":"9","author":"Mu\u02dcnoz-Organero Mario","year":"2023","journal-title":"Heliyon"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0299530","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T17:50:14Z","timestamp":1714067414000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0299530"}},"subtitle":[],"editor":[{"given":"Sudipta","family":"Chowdhury","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,4,25]]},"references-count":24,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4,25]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0299530","relation":{},"ISSN":["1932-6203"],"issn-type":[{"type":"electronic","value":"1932-6203"}],"subject":[],"published":{"date-parts":[[2024,4,25]]}}}