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SWMM模型中参数率定及局部灵敏度分析.docx

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SWMM模型中参数率定及局部灵敏度分析 Introduction TheStormWaterManagementModel(SWMM)isawidelyusedmodelingtoolforanalyzinganddesigningurbandrainagesystems.Itcansimulatethehydrological,hydraulic,andwaterqualityprocessesthatoccurinurbanwatersheds.However,itsaccuracyinpredictingthesystemresponseishighlydependentonthepropercalibrationofitsparameters.Parametercalibrationisaprocessofadjustingthemodelparameterstoimprovetheagreementbetweenthesimulatedandmeasuredsystemresponses.Inthispaper,wewilldiscusstheparametercalibrationprocessinSWMMandthelocalsensitivityanalysistechniquethatcanhelpidentifythesignificantparametersforcalibration. ParameterCalibration SWMMhasasetofdefaultparametersthatareusedintheabsenceofmeasureddata.However,thesedefaultparametersmaynotbeaccurateforaspecificurbandrainagesystem.Therefore,parametercalibrationisnecessarytoimprovetheaccuracyofthemodel.Thecalibrationprocessinvolvesadjustingthevaluesofthemodelparameterstooptimizetheagreementbetweenthesimulatedandmeasuredsystemresponses.Theoptimizationobjectivecanbetominimizetheerrorbetweenthesimulatedandmeasuredsystemresponses,suchastherootmeansquareerror(RMSE)ortheNash-Sutcliffeefficiency(NSE).Thecalibrationprocesscanbeperformedmanuallyorautomaticallyusingoptimizationalgorithms. Manualcalibrationinvolvesadjustingtheparametersbasedontheexperienceofthemodelerandthecomparisonofthesimulatedandmeasuredresponses.Themodeleradjuststheparametersstepbystepuntiltheagreementbetweenthesimulatedandmeasuredresponsesisimproved.Themanualcalibrationprocesscanbetime-consumingandsubjective,asitheavilyreliesonthemodeler'sexperienceandjudgment. Automaticcalibrationusesoptimizationalgorithmstoadjusttheparametersautomatically.Theoptimizationalgorithmsearchesforthebestcombinationofparametervaluesthatminimizestheobjectivefunction.TheobjectivefunctioncanbetheRMSEorNSE.SeveraloptimizationalgorithmshavebeendevelopedtocalibrateSWMM,suchasgeneticalgorithmsandparticleswarmoptimization.Theautomaticcalibrationprocesscansavetimeandimprovetheaccuracyofthecalibra