{"title":"Real-time prediction of waves using neural networks trained by particle swarm optimization","authors":"D. Gopinath, GS Dwarakish","doi":"10.1177/1759313116642896","DOIUrl":null,"url":null,"abstract":"This work investigates the strength of artificial neural network that is trained by an optimization technique called particle swarm optimization in the task of time series prediction of weekly and monthly significant wave heights. The suggested approach has been implemented at the location of New Mangalore Port in India. Three years of wave data measured during 2005–2007 are analyzed. It is found that the network trained with the help of the particle swarm optimization produces more accurate predictions of the significant wave heights and further with lesser amount of data than the traditionally trained feed-forward back-propagation network.","PeriodicalId":105024,"journal":{"name":"The International Journal of Ocean and Climate Systems","volume":"340 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The International Journal of Ocean and Climate Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1759313116642896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
This work investigates the strength of artificial neural network that is trained by an optimization technique called particle swarm optimization in the task of time series prediction of weekly and monthly significant wave heights. The suggested approach has been implemented at the location of New Mangalore Port in India. Three years of wave data measured during 2005–2007 are analyzed. It is found that the network trained with the help of the particle swarm optimization produces more accurate predictions of the significant wave heights and further with lesser amount of data than the traditionally trained feed-forward back-propagation network.