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1、JournalofHydrology385(2010)13–22ContentslistsavailableatScienceDirectJournalofHydrologyjournalhomepage:www.elsevier.com/locate/jhydrolStatisticaldownscalingofdailyprecipitationusingsupportvectormachinesandmultivariateanalysisShien-TsungChen,Pao-ShanYu*,Yi-HsuanTangDe
2、partmentofHydraulicandOceanEngineering,NationalChengKungUniversity,Tainan701,TaiwanarticleinfosummaryArticlehistory:Downscalinglocaldailyprecipitationfromlarge-scaleweathervariablesisoftennecessarywhenstudy-Received25February2009inghowclimatechangeimpactshydrology.Th
3、isstudyproposesatwo-stepstatisticaldownscalingReceivedinrevisedform15January2010methodforprojectionofdailyprecipitation.ThefirststepisclassificationtodeterminewhethertheAccepted25January2010dayisdryorwet,andthesecondisregressiontoestimatetheamountofprecipitationconditi
4、onalontheoccurrenceofawetday.Predictorsofclassificationandregressionmodelsareselectedfromlarge-ThismanuscriptwashandledbyscaleweathervariablesinNECPreanalysisdatabasedonstatisticaltests.Theproposedstatisticaldown-K.Georgakakos,Editor-in-Chief,withtheassistanceofA.A.Ts
5、onis,AssociateEditorscalingmethodisdevelopedaccordingtotwomethodologies.Onemethodologyissupportvectormachine(SVM),includingsupportvectorclassification(SVC)andsupportvectorregression(SVR),andKeywords:theotherismultivariateanalysis,includingdiscriminantanalysis(forclass
6、ification)andmultipleregres-Statisticaldownscalingsion.Thepopularstatisticaldownscalingmodel(SDSM)isanalyzedforcomparison.AcomparisonofDailyprecipitationdownscalingresultsintheShih-MenReservoirbasininTaiwanrevealsthatoverall,theSVMreproducesSupportvectormachinemostrea
7、sonabledailyprecipitationproperties,althoughtheSDMSperformsbetterthanothermodelsinSDSMsmalldailyprecipitation(lessthanabout10mm).Finally,projectionoflocaldailyprecipitationisper-formed,andfutureworktoadvancethedownscalingmethodisproposed.Ó2010ElsevierB.V.Allrightsres
8、erved.Introductiongenerators(e.g.,SelkerandHaith,1990;TungandHaith,1995;Yuetal.,2002),resamplingmethods(e.g.,Murphy,2000;BuishandCl
