Wave height forecasting in Dayyer, the Persian Gulf
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Etemad-Shahidi, A
Kazeminezhad, MH
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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. &
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Ocean Engineering
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38
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1
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© 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.
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Oceanography
Civil engineering
Maritime engineering
Maritime engineering not elsewhere classified