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1、JournalofWaterResourcesResearch水资源研究,2012,1,340-346doi:10.4236/jwrr.2012.15052PublishedOnlineOctober2012(http://www.hanspub.org/journal/jwrr.html)StudyonthePrecipitationForecastBasedonBP-Neural*NetworkandWaveletAnalysis123HaijingXiong,ShichengWang,DongWang1AnhuiHuaiRiverWaterTechnologyLimi
2、tedCompany,Bengbu2HydrologicalBureau,HuaiheRiverCommission,Bengbu3DepartmentofHydrosciences,SchoolofEarthSciencesandEngineering,StateKeyLaboratoryofPollutionControlandResourceReuse,NanjingUniversity,NanjingEmail:xionghaijing@126.comththstReceived:Jul.29,2012;revised:Aug.14,2012;accepted:Se
3、p.1,2012Abstract:JiangsuprovinceislocatedintheYangtzeandHuaiheRiverbasins,whichisoneoftheareasmostseverelyaffectedbydroughtsandfloods.Therefore,itisimportanttostudyontheprecipitationforecastinthisareaforthefloodcontrolwithdroughtrelief.Inthispaper,theprecipitationforecastmodelbasedonBPNeur
4、alNetworkandWaveletNeuralNetworkisestablishedwiththeprecipitationdatafromfourrainfallsta-tionsinJiangsuProvince,whichincludetheXuzhou,Ganyu,DongtaiandNanjingStations.Thefollowingconclusionscanbeobtainedbytheexampleanalysis:1)TheminimumrelativeerrorofBPneuralnetworkis1.16%,themaximumrelativ
5、eerroris16.35%,thedeterminecoefficientis0.87,themeansquareerroris4.27%;2)TheminimumrelativeerrorofWNNneuralnetworkis0.7%,themaximumrelativeerroris88.65%,thedeterminecoefficientis0.94,themeansquareerroris4.2%.Theresultsshowthat:1)Itisfeasi-bletoapplythebackpropagationneuralnetworkprecipitat
6、ionforecastmodel.Toacertainextent,thismodelcanreflectthetrendsoftheprecipitation;2)ThereismoreerrorofWNNinsomeyears,soavarietyofforecastingmethodsinpracticeareusedtomutualauthenticationandmutualcheckingtoimproveforecastaccuracy.Keywords:BackPropagationNeuralNetwork;WaveletNeuralNetwork;Ann
7、ualPrecipitation;RainfallForecast*BP神经网络和小波分析在年降水预报中的应用研究123熊海晶，王式成，王栋1安徽淮河水资源科技有限公司，蚌埠2淮河水利委员会水文局，蚌埠3南京大学地球科学与工程学院水科学系，南京Email:xionghaijing@126.com收稿日期：2012年7月29日；修回日期：2012年8月14日；录用日期：2012年9月1日摘要：江苏省地处江淮流域，是受旱涝灾害影响最为严重的地区之一。在该地区开展降水预报的研究，对防汛抗旱具有重要意义。本文采用江苏省徐州
