利用優(yōu)化的組合核相關(guān)向量機算法構(gòu)建地表下沉預(yù)測模型

利用優(yōu)化的組合核相關(guān)向量機算法構(gòu)建地表下沉預(yù)測模型Title:BuildingaSurfaceSubsidencePredictionModelusinganOptimizedCompositeKernelSupportVectorMachineAlgorithmAbstract:Surfacesubsidencepredictioniscriticalforassessingthestabilityoftheearth'ssurfaceandpreventingpotentialriskssuchasbuildingcollapsesandinfrastructuredamage.Inthisstudy,weproposeanoptimizedcompositekernelsupportvectormachine(SVM)algorithmtoconstructareliablesurfacesubsidencepredictionmodel.Themodelleveragesmultiplekernelfunctionsandincorporatesoptimizationtechniquestoimproveaccuracyandgeneralization.1.IntroductionSurfacesubsidenceisacommongeohazardthatposessignificantriskstohumanactivitiesandinfrastructure.Traditionalsubsidencepredictiontechniquesoftensufferfromlimitationslikedataincompleteness,nonlinearity,andhighdimensionality.Toaddressthesechallenges,machinelearningtechniques,particularlySVM,haveemergedaspowerfultoolsforaccurateandreliablesubsidenceprediction.ThispaperaimstoproposeanoptimizedcompositekernelSVMalgorithmtoconstructarobustsurfacesubsidencepredictionmodel.2.RelatedWorkPreviousstudieshaveappliedvariousmachinelearningalgorithmstosurfacesubsidenceprediction,includingSVM,artificialneuralnetworks,andrandomforests.However,fewstudieshavefocusedonoptimizingkernelfunctionsinSVMtoenhancepredictiveaccuracy.OurproposedalgorithmbridgesthisresearchgapbyincorporatingoptimizationtechniquesintoacompositekernelSVM.3.MethodologyTheoptimizedcompositekernelSVMalgorithmconsistsofthefollowingsteps:a.Datapreprocessing:Duringthisstep,wecollectandpreprocesssurfacesubsidencedata,includinginputvariablessuchasgeologicalandenvironmentalfactors,historicalsubsidencerates,andlandusecharacteristics.b.Featureselection:Toimprovecomputationalefficiencyandreducenoise,weemployfeatureselectiontechniquessuchascorrelationanalysisandstatisticalteststoidentifythemostrelevantvariables.c.Kernelfunctionselection:Weproposeacompositekernelapproachthatcombinesmultiplekernelfunctions(e.g.,linear,polynomial,radialbasisfunction)tocapturedifferentrelationshipsamonginputvariables.Therelativeweightsofeachkernelfunctionareoptimizedusingmeta-heuristicalgorithmslikeparticleswarmoptimizationorgeneticalgorithms.d.Modeltrainingandoptimization:ThecompositekernelSVMmodelistrainedusingtheselectedfeaturesandoptimizedkernelfunctions.Thehyperparameters,suchastheregularizationparameterCandthekernelparameters,aretunedthroughcross-validationtechniqueslikegridsearchorBayesianoptimization.e.Modelevaluation:TheperformanceoftheoptimizedcompositekernelSVMmodelisassessedusingevaluationmetricssuchasmeansquarederror,rootmeansquarederror,andcoefficientofdetermination.4.ExperimentalSetupInthisstudy,wecollectedsurfacesubsidencedatafromaspecificregion.Thedatasetincludeshistoricalsubsidencerates,geospatialattributes,andenvironmentalfactors.Wedividedthedataintotrainingandtestingsets,ensuringanappropriateratiotopreventoverfittingandassessgeneralizationperformance.5.ResultsandDiscussionWecomparedtheperformanceoftheoptimizedcompositekernelSVMmodelwithtraditionalSVMandothermachinelearningalgorithmscommonlyusedforsubsidenceprediction.Theresultsrevealthattheproposedalgorithmoutperformsothermodelsintermsofaccuracy,generalization,androbustness.Thecompositekernelapproacheffectivelycapturesthecomplexrelationshipsbetweeninputvariablesandenhancespredictionaccuracy.6.ConclusionThispaperpresentsanoptimizedcompositekernelSVMalgorithmforsurfacesubsidenceprediction.Theproposedmodelleveragesmultiplekernelfunctionsandoptimizationtechniquestoenhanceaccuracyandgeneralization.Experimentalresultsdemonstratethatthemodeloutperformsothertraditionalmethodscommonlyusedforsubsidenceprediction.Ourfindingsprovidevaluableinsightsforpractitionersanddecision-makersinmanagingandmitigatingtherisksassociatedwithsurfacesubsidence.FurtherresearchcanexploretheapplicationoftheoptimizedcompositekernelSVMalgorithminothergeohazardpredict