基于GRNN神经网络模型结合气溶胶消光系数和气象要素评估颗粒物质量浓度-豆柴文库

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基于GRNN神经网络模型结合气溶胶消光系数和气象要素评估颗粒物质量浓度
Title:EvaluationofParticulateMatterConcentrationbasedonGRNNNeuralNetworkModelincorporatingAerosolExtinctionCoefficientandMeteorologicalElements
Introduction:
Airpollution,particularlythepresenceofparticulatematter(PM),hasbecomeasignificantenvironmentalconcernworldwide.PMreferstoamixtureofsolidparticlesandliquiddropletssuspendedintheair.Thequalityofaircanhavedetrimentaleffectsonhumanhealth,climate,andvisibility.Therefore,accuratelyassessingPMconcentrationiscrucialforairqualitymonitoringandforecasting.Inthispaper,weproposetoutilizeaGRNN(GeneralizedRegressionNeuralNetwork)modeltoestimatePMconcentration,incorporatingbothaerosolextinctioncoefficientandmeteorologicalelements.
Methods:
1.DataCollection:
-ObtaindataonPMconcentration,aerosolextinctioncoefficient,andmeteorologicalelements(suchastemperature,humidity,windspeed,andwinddirection).
-Collectdatafrommultiplemonitoringstationstoensurearepresentativesample.
2.DataPreprocessing:
-Cleanthedatabyremovinganymissingorerroneousvalues.
-Normalizethedatatoensurethatallfeatureshaveasimilarmagnitude.Standardizationcanbeperformedusingtechniquessuchasmin-maxscalingorZ-scorenormalization.
3.ModelDevelopment:
-TheGRNNmodelischosenduetoitscapabilitytocapturecomplexrelationshipsbetweeninputvariablesandoutput.
-Splitthedatasetintotrainingandtestingsets.Thetrainingsetwillbeusedtotraintheneuralnetwork,whilethetestingsetwillbeusedtoevaluatemodelperformance.
-TraintheGRNNmodelusingthetrainingdataset.
-Tunethehyperparameters(e.g.,learningrate,numberofhiddenlayers)usingtechniqueslikegridsearchorrandomsearchtooptimizemodelperformance.
4.ModelEvaluation:
-Usethetestingsettoevaluatethemodel'sperformance.Commonevaluationmetricsincludemeansquareerror(MSE),meanabsoluteerror(MAE),andcoefficientofdetermination(R-squared).
-ComparethepredictedPMconcentrationswiththeactualvaluestodeterminetheaccuracyoftheGRNNmodel.
-Conductstatisticalanalysis,suchascorrelationanalysis,toexaminetherelationshipsbetweenaerosolextincti