基于BP神经网络的污染场地土壤重金属和PAHs含量预测-豆柴文库

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基于BP神经网络的污染场地土壤重金属和PAHs含量预测
Title:PredictionofHeavyMetalandPAHsContaminationinSoilofPollutedSitesusingBPNeuralNetwork
Introduction:
Soilcontaminationwithheavymetalsandpolycyclicaromatichydrocarbons(PAHs)isaglobalenvironmentalconcern.Thesepollutantscanhavedetrimentaleffectsonhumanhealthandtheecosystem,andthus,accuratepredictionoftheirconcentrationsinsoilisessentialforeffectiveremediationstrategies.Inrecentyears,artificialintelligencetechniques,suchastheBackpropagation(BP)neuralnetwork,haveemergedaspowerfultoolsforenvironmentalforecastingandmodeling.ThispaperaimstopresentacomprehensivereviewoftheuseofBPneuralnetworksforthepredictionofheavymetalandPAHscontaminationinsoilofpollutedsites.
Methodology:
TheBPneuralnetworkisatypeoffeed-forwardartificialneuralnetworkthatcanbeusedforbothregressionandclassificationtasks.Itconsistsofmultiplelayersofinterconnectedartificialneurons,eachwithassociatedweightsandactivationfunctions.Thenetworklearnsfromasetofinput-outputtrainingdatabyiterativelyupdatingtheweightsthroughaprocesscalledbackpropagation,whichminimizesthedifferencebetweenthepredictedandactualoutputs.
TopredictheavymetalandPAHscontaminationinsoil,aBPneuralnetworkmodelcanbedevelopedbyconsideringsoilproperties(e.g.,pH,organicmattercontent,cationexchangecapacity)asinputvariablesandheavymetal/PAHsconcentrationsastargetvariables.Themodelcanbetrainedusinghistoricaldatafrompollutedsites,wheresoilsampleshavebeenanalyzedforheavymetalandPAHscontent.ThetrainedmodelcanthenbeusedtopredicttheconcentrationsofheavymetalsandPAHsinsoilsamplesfromnewsites.
ResultsandDiscussion:
TheaccuracyandperformanceoftheBPneuralnetworkmodelforpredictingheavymetalandPAHscontaminationinsoilcanbeevaluatedusingstatisticalmetricssuchasmeansquarederror,correlationcoefficient,androotmeansquarederror.Theresultscanbecomparedwiththoseobtainedfromotherpredictivemodels,suchasmultiplelinearregressionorsupportvectormachines,toassessthesuperiorityoftheBPneuralnetworkapproach.
TheadvantagesofusingaBPneuralnetworkforthisp