Sooyoul Kim, Masahide Takeda, Chisato Hara, Hajime Mase
{"title":"基于长短期记忆与神经网络的海洋建筑前一日海浪预测","authors":"Sooyoul Kim, Masahide Takeda, Chisato Hara, Hajime Mase","doi":"10.9753/icce.v37.waves.26","DOIUrl":null,"url":null,"abstract":"Recently, marine construction work has increased under complex and strict conditions for large-scale marine sites and facilities. Accurate wave information at the work site is critical to conducting the marine construction safely. In particular, making decisions for the execution of the operation need a significant wave height ranging from 0.5 to 1.0 m as a threshold. However, studies on highly accurate real-time wave height predictions around the threshold are few. The present study developed a hybrid algorithm for real-time wave prediction by combining long-short term memory (LSTM) and artificial neural network (ANN) for the Hitachinaka Port, Japan.","PeriodicalId":497926,"journal":{"name":"Proceedings of ... Conference on Coastal Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ONE DAY AHEAD WAVE PREDICTIONS USING A HYBRID ALGORITHM OF LONG-SHORT TERM MEMORY AND NEURAL NETWORK FOR MARINE CONSTRUCTIONS\",\"authors\":\"Sooyoul Kim, Masahide Takeda, Chisato Hara, Hajime Mase\",\"doi\":\"10.9753/icce.v37.waves.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, marine construction work has increased under complex and strict conditions for large-scale marine sites and facilities. Accurate wave information at the work site is critical to conducting the marine construction safely. In particular, making decisions for the execution of the operation need a significant wave height ranging from 0.5 to 1.0 m as a threshold. However, studies on highly accurate real-time wave height predictions around the threshold are few. The present study developed a hybrid algorithm for real-time wave prediction by combining long-short term memory (LSTM) and artificial neural network (ANN) for the Hitachinaka Port, Japan.\",\"PeriodicalId\":497926,\"journal\":{\"name\":\"Proceedings of ... Conference on Coastal Engineering\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of ... Conference on Coastal Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.9753/icce.v37.waves.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of ... Conference on Coastal Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9753/icce.v37.waves.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ONE DAY AHEAD WAVE PREDICTIONS USING A HYBRID ALGORITHM OF LONG-SHORT TERM MEMORY AND NEURAL NETWORK FOR MARINE CONSTRUCTIONS
Recently, marine construction work has increased under complex and strict conditions for large-scale marine sites and facilities. Accurate wave information at the work site is critical to conducting the marine construction safely. In particular, making decisions for the execution of the operation need a significant wave height ranging from 0.5 to 1.0 m as a threshold. However, studies on highly accurate real-time wave height predictions around the threshold are few. The present study developed a hybrid algorithm for real-time wave prediction by combining long-short term memory (LSTM) and artificial neural network (ANN) for the Hitachinaka Port, Japan.