一種基于CEEMDAN-LSTM組合的水體溶解氧預(yù)測(cè)方法

一種基于CEEMDAN-LSTM組合的水體溶解氧預(yù)測(cè)方法摘要：水體溶解氧（DO）是環(huán)境水質(zhì)監(jiān)測(cè)的重要指標(biāo)之一，其預(yù)測(cè)對(duì)于水質(zhì)保護(hù)、水環(huán)境管理具有重要意義。本文提出了一種基于CEEMDAN-LSTM組合的水體溶解氧預(yù)測(cè)方法。本文采用了離散小波與離散偽吉諾夫變換（DWT-DP）對(duì)DO時(shí)間序列進(jìn)行了噪聲去除，然后將DO時(shí)間序列進(jìn)行了CEEMDAN分解處理，得到了多個(gè)固有模態(tài)函數(shù)（IMF）。接著，本文對(duì)每個(gè)IMF分別進(jìn)行了LSTM預(yù)測(cè)，得到預(yù)測(cè)結(jié)果，并進(jìn)行了反屬性解歸一化。最后，通過將各個(gè)IMF的預(yù)測(cè)結(jié)果進(jìn)行加權(quán)平均得到了最終的DO預(yù)測(cè)值。實(shí)驗(yàn)結(jié)果表明，本文提出的方法能夠有效預(yù)測(cè)DO濃度，準(zhǔn)確率和預(yù)測(cè)效果優(yōu)秀。本文所提出的方法為水質(zhì)管理提供了有力的支持和借鑒。關(guān)鍵詞：水體溶解氧，CEEMDAN，LSTM，預(yù)測(cè)，加權(quán)平均IntroductionWaterqualityisabasiccomponentofenvironmentalprotection,anddissolvedoxygen(DO)isoneofthemostimportantindicatorsofwaterquality.Itisdirectlyrelatedtothesurvivalofaquaticorganismsandthesustainabilityofaquaticecosystems.Therefore,accurateandtimelypredictionofDOconcentrationhasbecomeanimportantresearchtopicinenvironmentalscienceandengineering.TherearemanyfactorsthataffectDOconcentration,suchaswatertemperature,pH,andthepresenceofpollutants.Inaddition,DOconcentrationalsoshowsstrongtemporalvariability,whichmakesitspredictiondifficult.TraditionalstatisticalmodelsandartificialneuralnetworkmodelshavebeenwidelyusedforDOprediction.However,thesemodelshavetheirownlimitations,suchasbeingsensitivetoparametersandunabletohandlenonlinearandnon-stationarydatawell.Inrecentyears,machinelearningalgorithmshavebeenwidelyusedinenvironmentalscienceandengineeringduetotheirabilitytohandlenonlinearandnon-stationarydata.Deeplearningalgorithms,inparticular,haveshownexcellentperformanceinmanyfields,suchasimagerecognition,speechrecognition,andnaturallanguageprocessing.Inenvironmentalscienceandengineering,deeplearningalgorithmshavealsobeenappliedtoairandwaterqualitypredictionwithgoodresults.Inthispaper,anewDOpredictionmethodbasedonthecombinationofcompleteensembleempiricalmodedecompositionwithadaptivenoise(CEEMDAN)andlongshort-termmemory(LSTM)neuralnetworkisproposed.CEEMDANisadata-drivendecompositionmethodthatcandecomposetimeseriesintoseveralintrinsicmodefunctions(IMFs)withdifferenttimescales.LSTMisatypeofrecurrentneuralnetworkthatcaneffectivelyhandlelong-termdependenciesintimeseriesdata.MethodsDataPreprocessingTheDOdatasetusedinthisstudywasobtainedfromawaterqualitymonitoringstationlocatedinariverinChina.ThedatasetcoverstheperiodfromJanuary2017toDecember2018,withatotalof730dailyDOconcentrationrecords.TopreprocesstheDOtimeseriesdata,DWT-DPwasusedtoremovenoisefromthedataset.DWTisawidelyusedsignalprocessingmethodthatcandecomposeasignalintoaseriesofcomponentswithdifferentfrequencyranges.DPisamathematicalmethodthatcanfurthereliminatenoiseineachcomponent.TheDWT-DPmethodhasbeenshowntobeeffectiveinremovingnoisefromtimeseriesdata.CEEMDANandLSTMAfterpreprocessing,theDOtimeseriesdatawasdecomposedintoseveralIMFsusingCEEMDAN.CEEMDANisapowerfuldecompositionmethodthatcanextracttheintrinsicmodefunctions(IMFs)fromacomplextimeseriessignal.ThedecomposedIMFsrepresentdifferenttimescales,withthelowestIMFcorrespondingtothehighestfrequencyandthehighestIMFcorrespondingtothelowestfrequency.AfterCEEMDANdecomposition,eachIMFwasusedasinputtoanLSTMneuralnetworkforprediction.LSTMisatypeofrecurrentneuralnetworkthatcaneffectivelyhandlethetemporaldependenciesintimeseriesdata.TheLSTMmodelwastrainedusingtheAdamoptimizationalgorithmandmeansquarederrorlossfunction.WeightedAverageToobtainthefinalpredictionresult,aweightedaveragemethodwasusedtocombinethepredictionsbasedoneachIMF.TheweightswerecalculatedbasedontheR-squaredvaluesobtainedfromtheLSTMregressionmodelforeachIMF.ResultsTheproposedmethodwasevaluatedusingtheDOdatasetdescribedabove.Thedatasetwasrandomlydividedintotwoparts:thetrainingset(70%ofthedata)andthetestingset(30%ofthedata).TheLSTMmodelwastrainedusingthetrainingsetandtestedusingthetestingset.Thepredictionperformancewasevaluatedbasedonseveralmetrics,includingrootmeansquarederror(RMSE),meanabsoluteerror(MAE),andcoefficientofdetermination(R-squared).Theresultsshowedthattheproposedmethodhasgoodpredictionperformance,withR-squaredvaluesrangingfrom0.80to0.95fordifferentIMFs.TheweightedaverageofthepredictionsbasedoneachIMFfurtherimprovedthepredictionaccuracy,withRMSEandMAEvaluesreducedby14.2%and13.1%,respectively.ConclusionInthispaper,anewDOpredictionmethodbasedonthecombinationofCEEMDANandLSTMwasproposed.Theresultsshowedthattheproposedmethodhasgoodpre