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1、WATERRESOURCESRESEARCH,VOL.47,W02534,doi:10.1029/2009WR008944,2011Impreciseprobabilisticevaluationofsewerfloodinginurbandrainagesystemsusingrandomsettheory111,21GuangtaoFu,DavidButler,Soon-ThiamKhu,andSiAoSunReceived24November2009;revised12November2010;accepted3December2010;published23F
2、ebruary2011.[1]Uncertaintyanalysisiswidelyappliedinwatersystemmodelingtoquantifypredictionuncertaintyfrommodelsanddata.Conventionalmethodstypicallyhandlevariouskindsofuncertaintyusingasinglecharacterizingapproach,beitprobabilitytheoryorfuzzysettheory.However,usingasingleapproachmaynotbe
3、appropriate,particularlywhenuncertaintiesareofdifferenttypes.Forexample,insewerfloodestimationproblems,randomrainfallvariablesareusedasmodelinputsandimpreciseorsubjectiveinformationisusedtodefinemodelparameters.Thispaperpresentsageneralframeworkforsewerfloodestimationthatenablessimultaneou
4、sconsiderationoftwotypesofuncertainty:randomnessfromrainfalldatarepresentedusingimpreciseprobabilitiesandimprecisionfrommodelparametersrepresentedbyfuzzynumbers.Thesetwotypesofuncertaintiesarecombinedusingrandomsettheoryandthenpropagatedthroughahydrodynamicurbandrainagemodel.Twopropagat
5、ionmethods,i.e.,discretizationandMonteCarlobasedmethods,arepresentedandcompared,withthelattershowntobemuchmorecomputationallyefficientandhencerecommendedforhigh-dimensionalproblems.Themodeloutput(flooddepth)isgeneratedintheformofloweranduppercumulativeprobabilities,whicharebestestimatesgi
6、venthevariousstochasticandepistemicuncertaintiesconsideredandwhichembracetheunknowntruecumulativeprobability.Thedistancebetweenthecumulativeprobabilitiesrepresentstheextentofimprecise,incomplete,orconflictinginformationandcanbereducedonlywhenmoreknowledgeisavailable.Theproposedmethodolog
7、yhasamorecompleteandthusmoreaccuraterepresentationofuncertaintyindataandmodelsandcaneffectivelyhandledifferentuncertaintycharacterizationsinasingle,integratedframeworkforsewerfloodestimation.Citation:Fu,G.,D.Butler,S.-T.Khu,andS.Sun(2011),Impreciseprobabilisticevaluationofsewerf
