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1、华中科技大学硕士学位论文AbstractRunoffforecastplaysanimportantpartinnotonlywaterrecoursesmanagement,butalsoeconomicaloperationofhydropowersystem,aswellasthecomprehensiveimpactonfloodcontrol,navigation,waterenvironmentandsustainabledevelopment.Moreovertheforecastaccuracyhasadirec
2、teffectonthehydropowerdistribution.Howeverthehydrologicsystemisalargeandcomplicatedsystemandtherunoffelementisinfluencedbyclimate,geographicenvironmentandhumanactivities,etc.Thereafter,itschangecharacteristicandrulearecomplicated,random,grayandnon-liner.Thismakesruno
3、ffforecast,especiallymid-and-long-termrunoffforecasthasbecomecomplicatedanddifficult,thetraditionalmethodsbasedonlineartheoriesaredifficulttomakeagreatprogressinhydrologicforecast.Itisnecessarytodevelopmorenewways.Artificialneuralnetworkhasbeenappliedtohydrologicfore
4、castwidelyduetothecapabilityofself-learning,self-organizingandself-adapting,butithasover-learning,localoptimizationandotherissues.However,supportvectormachine(SVM)avoidover-learningproblemeffectivelybecauseitisbasedonVapnik-Chervonenkis(VC)dimensiontheoryofstatistica
5、llearningtheoryandstructuralriskminimizationtheory;SVMadoptquadraticprogrammingandLagrangetheorytogetaglobaloptimization.Furthermoreitintroduceskernelfunctiontopredigestthesolutionofnon-linerquestion.Inconclusion,SVMwillbeappliedtorunoffforecast.First,thinkingaboutth
6、edifficultiesofthemid-and-long-termrunoffforecastandinordertoimprovetheforecastaccuracy,afterphysicallyreviewingtheforecastfactors,therightoneshavebeenchosenbystatisticallyanalysisandfuzzyoptimummethod.Second,theforecasthasbeendonebasesontheartificialneuralnetworkand
7、SVMmethodusingtherightfactorsandannuallyrunoffflow.Aftercontrastingandstudyingtheresult,theSVMisfitfortherunoffforecastcomplexityandimprovestherunoffforecastaccuracyinasmallsample.Keywords:Runoffforecast,Forecastfactors,ArtificialNeuralNetwork,SupportVectorMachineII华
8、中科技大学硕士学位论文目录摘要.................................................................................................................IAB
