Page 54 - 《水资源与水工程学报》2022年第4期
P. 54

!33 " ! 4 #                       & ' ( ) & * + , -                               Vol.33No.4
               2022 $ 8 %               JournalofWaterResources&WaterEngineering                 Aug.,2022

            DOI:10.11705/j.issn.1672-643X.2022.04.07


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                 FGHIJ:P641.134   KLMNO:A    KPQJ:1672643X(2022)04005008

                          PredictionofkarstspringwaterlevelbasedonBPneural
                                     networkoptimizedbygeneticalgorithm

                                    1,2,3
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                    CHENHuanliang      ,LIChangsuo      ,GAOShuai ,SUNBin        1,2,3 ,LINGuangqi
                   (1.801HydrogeologicalandGeoengineeringBrigade,ShandongProvincialBureauofGeology&MineralResources,
                    Jinan250014,China;2.ShandongEngineeringResearchCenterforEnvironmentalProtectionandRemediationon
                     Groundwater ,Jinan250014,China;3.KeyLaboratoryofGroundwaterResourcesandEnvironment,Shandong
                                  ProvincialBureauofGeology&MineralResources,Jinan250014,China)
                 Abstract:KarstspringsinnorthChinaareimportantnaturalresourceswithmultipleattributesofland
                 scape ,cultureandtourism,whichplayimportantrolesinthedevelopmentoflocaleconomyandsociety.
                 Monitoringdataofprecipitation ,groundwaterwithdrawalandartificialecologicalrechargeofBaotuSpring
                 from2016to2018arecollectedformodellingpurposesinordertoachievemoreaccuratepredictionre
                 sultsofdynamicchangesofthekarstspring.Meanwhile ,6typesofBPneuralnetworkandgeneticalgo
                 rithmoptimizedBPneuralnetworkareestablishedtopredictthewaterleveloftheBaotuSpring,andthen
                 thepredictionresultsarecomparedandevaluated.TheresultsshowthattheBPneuralnetworkoptimized
                 bygeneticalgorithmcanimprovethepredictionstability ,reducethemaximumiterationofneuralnetwork
                 andsaveagreatdealofcalculationcost ,comparedtoBPneuralnetwork.TheGA-BP(LM)neuralnet
                 workwhichadoptsLevenbergMarquardttrainingmethodismoresuitableforthepredictionofkarstspring
                 waterlevelduetoitsadvantagesofstableperformance,lowcalculationcostandsmallpredictionerror.
                 Thisresearchcanprovidereferencesforthedesignandimplementationofprotectionmeasuresforkarst
                 springsinnorthChina.

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