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1、WATERRESOURCESRESEARCH,VOL.32,NO.10,PAGES3033-3040,OCTOBER1996ModelingwaterretentioncurvesofsandysoilsusingneuralnetworksMarcelG.SchaapandWillemBoutenLandscapeandEnvironmentalResearchGroup,UniversityofAmsterdam,Amsterdam,NetherlandsAbstract.Weusedneuralnetworks(NNs)t
2、omodelthedryingwaterretentioncurve(WRC)of204sandysoilsamplesfromparticle-sizedistribution(PSD),soilorganicmattercontent(SOM),andbulkdensity(BD).Neuralnetworkscanrelatemultiplemodelinputdatatomultiplemodeloutputdatawithouttheneedofanapriorimodelconcept.Inthiswayahighp
3、erformanceblack-boxmodeliscreated,whichisveryusefulinadataexplorationefforttoassessthemaximumobtainablepredictionaccuracy.WeusedaseriesofNNmodelswithanincreasingparametrizationofinputandoutputvariablestogetabetterinterpretabilityofmodelresults.Inthefirsttwomodelsweus
4、edtheninePSDfractions,BD,andSOMasinput,whilewepredictedtheninepointsofthewaterretentioncurve.TheseNNshad12inputand9outputvariables,predictingWRCswithanaverageroot-mean-squareresidual(RMSR)watercontentof0.020cm3cm-3.Afterafewintermediarymodelswithincreasingparametriza
5、tionofPSDandWRCusing(adapted)vanGenuchten[1980]equationswearrivedatafinalNNmodelthatusedsixinputvariablestopredictthreevanGenuchten[1980]parametersresultinginaRMSRof0.024cm3cm-3.WefoundsaturatedandresidualwatercontentstobeunrelatedtothePSD,BD,orSOM,thereforethesatura
6、tedwatercontentwasconsideredtobeanindependentinputvariable,whiletheresidualwatercontentwassettozero.SensitivityanalysesshowedthatthePSDhadamajorinfluenceontheshapeoftheWRC,whileBDandSOMwerelessimportant.Onthebasisofthesesensitivityanalysesweestablishedmoreexplicitequ
7、ationsthatdemonstratedsimilarityrelationsbetweenPSDandWRCandincorporatedeffectsofSOMandBDinanempiricalway.Despitethefactthatweconsideredalargenumberoflinearandnonlinearvariantstheseequationshadaweakerperformance(RMSR:0.029cm3cm-3)thantheNNmodels,provingthemodelingpow
8、erofthattechnique.1.Introductiontheyusetheshapesimilaritybetweenpore-sizeandparticle-sizedistributionsbutalsorequireempiricalparame