Wave height forecasting in Dayyer, the Persian Gulf
Abstract
Forecastingofwaveparametersisnecessaryformanymarineandcoastaloperations.Different forecastingmethodologieshavebeendevelopedusingthewindandwavecharacteristics.Inthispaper, artificialneuralnetwork(ANN)asarobustdatalearningmethodisusedtoforecastthewaveheightforthe next 3,6,12and24hinthePersianGulf.Todeterminetheeffectiveparameters,differentmodelswith various combinationsofinputparameterswereconsidered.Parameterssuchaswindspeed,directionand wave heightoftheprevious3h,werefoundtobethebestinputs.Furthermore,usingthedifference between waveandwinddirectionsshowedbetterperformance.Theresultsalsoindicatedthatifonlythe wind parametersareusedasmodelinputstheaccuracyoftheforecastingincreasesasthetimehorizon increasesupto6h.Thiscanbeduetothelowerinfluenceofpreviouswaveheightsonlargerleadtime forecastingandtheexistinglagbetweenthewindandwavegrowth.Itwasalsofoundthatinshortlead times, theforecastedwaveheightsprimarilydependonthepreviouswaveheights,whileinlargerlead times thereisagreaterdependenceonpreviouswindspeeds. &
Journal Title
Ocean Engineering
Volume
38
Issue
1
Copyright Statement
© 2011 Elsevier Inc. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Subject
Maritime Engineering not elsewhere classified