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基于GA优选参数的SVR水质参数遥感反演方法.docx

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基于GA优选参数的SVR水质参数遥感反演方法 1.Introduction Waterqualityisavitalaspectofmaintaininghealthyaquaticecosystemsandensuringthewell-beingofhumanpopulations.However,conventionalmethodsformonitoringwaterqualityareoftenexpensive,labor-intensive,andtime-consuming.Remotesensingtechnologyoffersapromisingalternativeformonitoringwaterqualityparameters.Inrecentyears,supportvectorregression(SVR)hasproventobeapowerfultoolforwaterqualityparameterestimationusingremotesensingdata.However,theaccuracyofSVRmodelsstronglydependsontheselectionofappropriateinputparametersandhyperparameters.Inthisstudy,weproposeamethodologytooptimizetheselectionofparametersforSVR-basedwaterqualityparameterestimationusinggeneticalgorithms(GA). 2.Background Supportvectorregression(SVR)isasupervisedlearningalgorithmthathasbeenwidelyusedformodelingnon-linear,high-dimensionaldata.TheperformanceofSVRmodelsishighlydependentontheselectionofhyperparameters,especiallytheregularizationparameterCandkernelparameterγ.Theoptimalvaluesforthesehyperparameterscanbeobtainedthroughgridsearchorotheroptimizationtechniques.However,thesemethodsarecomputationallyexpensiveandmayresultinoverfitting. Geneticalgorithms(GA)areatypeofheuristicoptimizationtechniqueinspiredbybiologicalevolutionprocesses.GAcaneffectivelysearchforaglobaloptimalsolutioninalargesearchspaceandavoidlocaloptima.GAhasbeenappliedinvariousfields,includingparameterselectionformachinelearningmodels,featureselection,andoptimizationofcomplexsystems. 3.Methodology Theproposedmethodinvolvesthefollowingsteps: Step1:Datapreprocessing Remotesensingdataforwaterqualityestimationtypicallyincludesmultispectralorhyperspectraldata,aswellasinsituwaterqualitymeasurements.Thedatashouldbepreprocessedtoremovenoise,correctatmosphericeffects,andnormalizethedata. Step2:Featureselection Featureselectionreferstotheprocessofidentifyingthemostinformativefeaturesfromtheinputdata.Inthisstudy,weusedthecorrelation-basedfeatureselection(CFS)algorithmtoselectrelevantfeaturesbasedontheircorrelationwiththeta