Page 59 - 2023年第54卷第2期
P. 59

andsupportvectormachinebasedonquantum- behavedparticleswarm optimization[J].JournalofHydrology,
                      2020,583:124627.
                [11] WUCL,CHAUK W,LIY S,Methodstoimproveneuralnetworkperformanceindailyflowsprediction[J].
                       JournalofHydrology,2009,372(1?4):80 - 93.
                [12] WUCL,CHAUKW,LIYS.Predictingmonthlystreamflowusingdata - drivenmodelscoupledwithdata - pre
                       processingtechniques[J].WaterResourcesResearch,2009,45(8):2263 - 2289.
                [13] 梁浩,黄生志,孟二浩,等.基于多种混合模型的径流预测研究[J].水利学报,2020,51(1):112 - 125.
                [14] 孙娜,周建中.基于正则极限学习机的非平稳径流组合预测[J].水力发电学报,2018,37(8):20 - 28.
                [15] NOURANIV,BAGHANAM AH,ADAMOWSKIJ,etal.Applicationsofhybridwavelet - artificialintelligence
                       modelsinhydrology :Areview[J].JournalofHydrology,2014,514:358 - 377.
                [16] ZHANGX,PENGY,ZHANGC,etal.Arehybridmodelsintegratedwithdatapreprocessingtechniquessuitable
                       formonthlystreamflowforecasting?Someexperimentevidences[J].JournalofHydrology,2015,530:137 - 152.
                [17] NAPOLITANOG,SERINALDIF,SEEL.ImpactofEMDdecompositionandrandom initializationofweightsin
                       ANNhindcastingofdailystreamflowseries :Anempiricalexamination[J].JournalofHydrology,2011,406(3?4):
                      199 - 214.
                [18] TANQF,LEIXH,WANGX,etal.Anadaptivemiddleandlong - termrunoffforecastmodelusingEEMD - ANN
                       hybridapproach [J].JournalofHydrology,2018,567:767 - 780.
                [19] ZUOG,LUOJ,WANGN,etal.Decompositionensemblemodelbasedonvariationalmodedecompositionand
                       longshort - term memoryforstreamflowforecasting [J].JournalofHydrology,2020,585:124776.
                [20] QUILTYJ,ADAMOWSKIJ.Addressingtheincorrectusageofwavelet - basedhydrologicalandwaterresources
                       forecastingmodelsforreal - worldapplicationswithbestpracticesandanewforecastingframework [J].Journalof
                       Hydrology,2018,563:336 - 353.
                [21] FANGW,HUANGS,RENK,etal.Examiningtheapplicabilityofdifferentsamplingtechniquesinthedevelop
                       mentofdecompositionbasedstreamflowforecastingmodels [J].JournalofHydrology,2019,568:534 - 550.
                [22] JIANGY,HUANGG,YANGQ,etal.Anovelprobabilisticwindspeedpredictionapproachusingrealtimere
                       finedvariationalmodeldecompositionandconditionalkerneldensityestimation[J].EnergyConversionandMan
                       agement ,2019,185(4):758 - 773.
                [23] SANGYF,WANGZ,LIUC.ComparisonoftheMKtestandEMDmethodfortrendidentificationinhydrological
                       timeseries[J].JournalofHydrology,2014,510(3):293 - 298.
                [24] LIUH,MIX,LIY.Smartmulti - stepdeeplearningmodelforwindspeedforecastingbasedonvariationalmode
                       decomposition ,singularspectrumanalysis,LSTM networkandELM[J].EnergyConversion&Management,2018,
                      159:54 - 64.
                [25] DRAGOMIRETSKIYK,ZOSSOD.Variationalmodedecomposition[J].IEEETransactionsonSignalProcessing,
                      2014,62(3):531 - 544.
                [26] HOCHREITERS,SCHMIDHUBERJ.Longshort - termmemory[J].NeuralComputation.1997,9(8):1735 - 1780.
                [27] ADDISONPS.TheIllustratedWaveletTransformHandbook:introductorytheoryandapplicationsinsciencs,en
                       gineering,medicineandfinance - secondedition[M].BocaRaton:CRCPress,2016.
                [28] XIONGT,BAOYK,HUZY.DoesrestrainingendeffectmatterinEMD - basedmodelingframeworkfortimese
                       riesprediction ?Someexperimentalevidences[J].Neurocomputing,2014,123(S1):174 - 184.
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