一种基于CEEMDAN-LSTM组合的水体溶解氧预测方法

一种基于CEEMDAN-LSTM组合的水体溶解氧预测方法摘要：水体溶解氧（DO）是环境水质监测的重要指标之一，其预测对于水质保护、水环境管理具有重要意义。本文提出了一种基于CEEMDAN-LSTM组合的水体溶解氧预测方法。本文采用了离散小波与离散伪吉诺夫变换（DWT-DP）对DO时间序列进行了噪声去除，然后将DO时间序列进行了CEEMDAN分解处理，得到了多个固有模态函数（IMF）。接着，本文对每个IMF分别进行了LSTM预测，得到预测结果，并进行了反属性解归一化。最后，通过将各个IMF的预测结果进行加权平均得到了最终的DO预测值。实验结果表明，本文提出的方法能够有效预测DO浓度，准确率和预测效果优秀。本文所提出的方法为水质管理提供了有力的支持和借鉴。关键词：水体溶解氧，CEEMDAN，LSTM，预测，加权平均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