基于GA优选参数的SVR水质参数遥感反演方法-豆柴文库

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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