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1、UpdatingMethodsforRealTimeFloodForecasting:AComparisonthroughSenegalRiverBasinUpstreamSoussouSambou1,SeniTamba2,ClementDiatta1andCheikhMohamedFadelKebe31.DepartmentofPhysics,FacultyofScienceandTechnics,TheUniversityofCheikhAntaDIOP,Dakar99000,Senegal2.Depart
2、mentofCivilEngineering,ThePolytechnicalSchoolofThiès,Dakar99000,Senegal3.ThePolytechnicalHighSchool,Dakar99000,SenegalReceived:September2,2011/Accepted:November2,2011/Published:January20,2012.Abstract:HeavyfloodsoccurfrequentlyintheSenegalRiverBasin,causingc
3、atastrophicfloodingdownstreamtheriverratingstationofBakel.Anticipatingtheoccurrenceofsuchphenomenaistheonlywaytoreducetheresultingdamages.Floodforecastingisanecessity.Floodforecastingplays32alsoanimportantroleintheimplementationoffloodmanagementscenariosandi
4、ntheprotectionofhydroelectricstructures.Manymethodsareapplied.Themostcompletearebasedontheconservationlawsofphysicsgoverningthefreesurfaceflow.Thesemethodsneedacompletedescriptionofthegeometryoftheriverandtheirimplementationrequiresalsohugeinvestments.Inprac
5、ticetheriverbasincanbeconsideredasasystemofinputs-outputsrelatedbyatransferfunction.Inthispapertheauthorsfirstusedamultiplelinearregressionmodelwithconstantparametersestimatedbytheordinaryleastsquaremethodtosimulatethepropagationofthefloodsintheupstreamparto
6、ftheSenegalriverbasin.Theauthorsthenapplystatisticalandgraphicalcriteriaofgoodness-of-fittotestthesuitabilityofthismodel.Threeproceduresofparametersupdatinghavethenbeenaddedtothislinearmodel:theKalmanfiltermethod,therecursiveleastsquaremethod,andthestochasti
7、cgradientmethod.Thecriteriaofgoodness-of-fitusedabovehaveshownthatthe32stochasticgradientmethod,althoughmorerudimentary,representsbetterthefloodpropagationintheheadbasinoftheSenegalriverupstreamBakel.Thisresultisparticularlyinterestingbecausedatainfluencedby
8、ManantaliDamareused.Keywords:Hydrology,multiplelinearregressionmodels,Kalmanfiltering,recursiveleastsquares,stochasticgradient,floodforecasting,Senegalriverheadbasin.1.Introduction??Freesurfacef
