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改进粒子群算法优化SVR水质预测模型研究.docx

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改进粒子群算法优化SVR水质预测模型研究 Abstract Inrecentyears,supportvectorregression(SVR)hasgainedwidepopularityinwaterqualityprediction,owingtoitspowerfullearningabilityandhighpredictionaccuracy.However,theconventionalSVRmodeloftengetstrappedinlocaloptimizationandfailstofindouttheglobalextreme,therebyunderminingthepredictionperformance.Toovercomethislimitation,thispaperproposesanimprovedparticleswarmoptimization(PSO)algorithmforoptimizingSVRwaterqualitypredictionmodel.Theproposedalgorithmenhancestheselectionandupdatingstrategyofparticlestoexplorethewholesearchspaceandconvergetowardstheglobaloptimum.Theeffectivenessoftheproposedalgorithmwasdemonstratedbyconductingexperimentsonareal-worlddataset,andtheresultsshowedthatitoutperformedthestandardPSOandconventionalSVRmodelsregardingvariousevaluationmetrics,suchasmeanabsoluteerror(MAE),rootmeansquarederror(RMSE)andcoefficientofdetermination(R2). Introduction Waterqualitypredictionplaysanessentialroleinensuringthesafetyandsecurityofhumanhealthandtheenvironment.Withtheever-increasingdeteriorationofwaterquality,thereisanurgentneedforeffectivewaterqualitypredictionmodelsthatcanenabletimelyandaccuratedecisionsforwaterresourcemanagement.Variouspredictivemodelshavebeendevelopedforwaterqualityprediction,amongwhichsupportvectorregression(SVR)hasemergedasoneofthemosteffectiveandefficientmodelsduetoitsstronggeneralizationabilityandhighpredictionaccuracy.However,theperformanceoftheSVRmodelsignificantlydependsontheselectionofkernelfunctionandtuningofparametersthatneedtobeoptimized.Thisoptimizationofteninvolvesacomplexandchallengingsearchproblemthatrequiresapowerfuloptimizationalgorithmtoefficientlyandeffectivelyidentifytheoptimalvalues. Particleswarmoptimization(PSO)isawidelyadoptedoptimizationalgorithminvariousfieldsbecauseofitsfastconvergencerate,simplicity,andglobalsearchcapability.Overtheyears,severalresearchershavecombinedPSOwithSVRtoenhancethepredictionaccuracyofthemodel.However,theconventionalPSOhaslimitationsconcerninglocaloptimization,anditma