Page 211 - 《水资源与水工程学报》2024年第6期
P. 211
!35 "!6 # & ' ( ) & * + , - Vol.35No.6
2024 $ 12 % JournalofWaterResources&WaterEngineering Dec.,2024
DOI:10.11705/j.issn.1672-643X.2024.06.21
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HIJKL:TP181;P426.616 MNOPQ:A MRSL:1672643X(2024)06020713
Integratedmodelingfordroughtmonitoringbasedonmultisourcedata
andmachinelearningalgorithm:acasestudyofHebeiProvince
WANGXiao,LIUHaixin,SUNZhenyu,WANGJialin,ZHUYan
(SchoolofMiningandGeomaticsEngineering,HebeiUniversityofEngineering,Handan056038,China)
Abstract:Basedonremotesensing,reanalysisandsoilmoisturesitedata,anintegrateddroughtmonito
ringmodelonmonthlyscalewasconstructedforvegetationofgrowingseasonsindifferentecologicalzones
ofHebeiProvincefrom2001to2022.Fourclassicmachinelearningmethods ,namely,supportvector
machine(SVM),random forest(RF),radialbasisfunctionneuralnetwork(RBFNN)andextreme
learningmachine (ELM)wereadoptedforthemodeling.ThencombinedwithSen’sslopeanalysis,the
spatialvariationofdroughtinHebeiProvincewasrevealed.Theresultsshowedthatthefittingeffectof
RF ,RBFNNandSVMpresentedstrongstabilityindifferentmonthsofthegrowingseason,aswellasin
differentecologicalzones ,whilethatofELMwasrelativelypoor.Therefore,theintegratedRFRBFNN
SVM modelwasselectedbasedondifferentecologicalzonesforthemodelingofcomprehensivedrought
monitoringinHebeiProvince.Theapplicationofthemodelindicatedthatitsfittingperformancewasbet
terinthewetenvironment,andinthemiddleofthevegetationgrowingseason.Theaverageaccuracyof
themodelwas85.15%,andthepredictedvaluewasingoodagreementwiththemeasuredvalue.The
averagespatialvariationof SM10was0.174/ainthestudyperiod,andsoilrelativehumidityin77.21%
oftheareashowedanupwardtrend ,andthedroughtsituationwasalleviated.
Keywords:multisourcedata;soilrelativehumidity;machinelearning;droughtmonitoring;Hebei
Province
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