Page 94 - 《水资源与水工程学报》2023年第1期
P. 94

9 0                      & ' ( ) & * + , -                 2023 $

            2 VMD§<>X»­/—«T»Ú, ¾ô72Þ                             osciencesandEngineering ,2022,19(2):1633-1648.
            žÈi4Fô7„ˆ, ¥¦)¹uÈɝž56                           [12]DRAGOMIRETSKIYK,ZOSSOD.Variationalmodede
            [¿, ab, ·§ª|S>X·¸ VMD»­$P。                           composition [J].IEEETransactionsonSignalProcessing,
                                                                   2013,62(3):531-544.
                 ( 3) »K56Þë·ßàÈÉ5Ê[¿, OP
                                                               [13]DINGJiakai,XIAODongming,LIXuejun.Gearfaultdi
            È$±P¯{G…øðT»Ú56ž。U
                                                                   agnosisbasedongeneticmutationparticleswarmoptimiza
            U2 ELMANžš¯ CNN-LSTMžÌ‚Î
                                                                   tionVMDandprobabilisticneuralnetworkalgorithm [J].
            K»Ú56, <+ž[¿)³ÈÉ, ßÅ EL
                                                                   IEEEAccess ,2020,8:18456-18474.
            AMNžÍ CNN-LSTMž¢ûG…‚ÎK                          [14]ZHANGFangqim,KANGYan,CHENGXiao,etal.A
            »Ú56。                                                 hybridmodelintegratingElmanneuralnetworkwithvaria
                                                                   tionalmodedecompositionandBox-Coxtransformation
            ©ªKL:
                                                                   formonthlyrunofftimeseriesprediction [J].WaterRe
            [1]Ëáá, ³ ?, !)·, ]. „…G_÷»¼ - E"š
                                                                   sourcesManagement ,2022,36:3673-3697.
                › - =!fÚ­êéMžê'c_Nª|S56
                                                               [15]SIBTAINM,LIXianshan,NABIG,etal.Development
                [J]. ?@3#X€,2020,35(2):355-364.
                                                                   ofathreestagehybridmodelbyutilizingatwostagesig
            [2]l Ô, …‚. „…c%gùeV”ösU|SmM
                                                                   naldecompositionmethodologyandmachinelearningap
                56[J]. 1Ä<ÄeÊ,2018,37(8):20-28.
                                                                   proachtopredictmonthlyrunoffatSwatRiverBasin ,Pa
            [3]BAJIRAOTS,KUMARP,KUMARM,etal.Potentialof
                                                                   kistan[J].DiscreteDynamicsinNatureandSociety,
                hybridwaveletcoupleddatadrivenbasedalgorithmsfordaily
                                                                   2020,2020:7345676.
                runoffpredictionincomplexriverbasins [J].Theoreticaland
                                                               [16]LIUHongchi,LIPeng,LIMeng,etal.Loadprediction
                AppliedClimatology ,2021,145:1207-1231.
                                                                   basedonhybridmodelofVMD-mRMR-BPNN-LSS
            [4]FRAMEJ,KRATZERTF,KLOTZD,etal.Deeplearn
                                                                   VM[J].Complexity,2020,2020:6940786.
                ingrainfall-runoffpredictionsofextremeevents [J].Hy
                                                               [17]ZHANGGang,LIUHongchi,ZHANGJiangbin,etal.Wind
                drologyandEarthSystemSciencesDiscussions ,2022,26
                                                                   powerpredictionbasedonvariationalmodedecomposition
                (13):3377-3392.
                                                                   multifrequencycombinations[J].JournalofModernPower
            [5]HEXinxin,LUOJungang,LIPeng,etal.Ahybridmodel
                                                                   SystemsandCleanEnergy ,2019,7:281-288.
                basedonvariationalmodedecompositionandgradientboos
                                                               [18]LIUWei,CAOSiyuan,CHENYangkang.Applicationsof
                tingregressiontreeformonthlyrunoffforecasting[J].Wa
                                                                   variationalmodedecompositioninseismictimefrequencya
                terResourcesManagement,2020,34:865-884.
                                                                   nalysis[J].Geophysics,2016,81(5):V365-V378.
            [6]KUMARS,TIWARIMK,CHATTERJEEC,etal.Reservoir
                                                               [19]KIM T-Y,CHO S-B.Predictingresidentialenergy
                inflowforecastingusingensemblemodelsbasedonneuralnet
                                                                   consumptionusingCNN-LSTMneuralnetworks[J].En
                works ,waveletanalysisandbootstrapmethod[J].WaterRe
                                                                   ergy,2019,182:72-81.
                sourcesManagement ,2015,29:4863-4883.
                                                               [20] HOCHREITER S,SCHMIDHUBER J.Longshortterm
            [7]CHENShu,RENMiaomiao,SUNWei.Combiningtwo
                                                                   memory [J].NeuralComputation,1997,9(8):1735-1780.
                stagedecompositionbasedmachinelearningmethodsfor
                                                               [21]ELMANJL.Findingstructureintime[J].CognitiveSci
                annualrunoffforecasting [J].JournalofHydrology,2021,
                                                                   ence ,1990,14(2):179-211.
                603:126945.
                                                               [22]Æ#X, ­$, Kä†. „… VMD-CNN-LSTM
            [8]SAMANTARAYS,SAHOOA.Estimationofrunoffthrough
                                                                   žô'Sqª|S56[J]. õ1\%ieeÊ,
                BPNNandSVM inAgalpurWatershed [M]//DITZINGER
                                                                   2021,37(1):1-8.
                T.AdvancesinIntelligentSystemsandComputing.Berlin :
                                                               [23]LIVIERISIE,PINTELASE,PINTELASP.ACNN-LSTM
                Springer,2020.
                                                                   modelforgoldpricetimeseriesforecasting[J].NeuralCom
            [9]HINTONGE,OSINDEROS,TEHY-W.Afastlearning
                                                                   putingandApplications,2020,32:17351-17360.
                algorithmfordeepbeliefnets [J].NeuralComputation,
                                                               [24]LUWenjie,LIJiazheng,LIYifan,etal.ACNN-LSTM
                2006,18(7):1527-1554.
                                                                   basedmodeltoforecaststockprices[J].Complexity,
            [10]©2, êVs, ³ÿ4, ]. „… CNN-LSTMõ¬
                                                                   2020,2020:6622927.
                 öĶÐ56[J]. œÞ›€³,2020,41(5):37-41.
                                                               [25]MUZAFFARS,AFSHARIA.Shorttermloadforecastsu
            [11]JINGXin,LUOJungang,ZHANGShangyao,etal.Run
                                                                   singLSTM networks [J].EnergyProcedia,2019,158:
                 offforecastingmodelbasedonvariationalmodedecompo
                                                                   2922-2927.
                 sitionandartificialneuralnetworks[J].MathematicalBi
   89   90   91   92   93   94   95   96   97   98   99