一种以神经网络为支撑的地下水位预测方法-豆柴文库

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一种以神经网络为支撑的地下水位预测方法
Title:ANeuralNetwork-basedApproachforGroundwaterLevelPrediction
Abstract:
Groundwaterisavitalandfiniteresourcethatplaysacrucialroleinvarioussectorssuchasagriculture,industry,anddomesticwatersupply.Theaccuratepredictionofgroundwaterlevelsisessentialforeffectivewaterresourcemanagementanddecision-making.Thispaperproposesanovelapproachthatutilizesneuralnetworksasaframeworkforpredictinggroundwaterlevels.Theobjectiveistodevelopanaccurateandreliablemodelthatcancapturethecomplexrelationshipsbetweenvariousinfluencingfactorsandgroundwaterlevels.
Introduction:
Groundwaterlevelpredictionisachallengingtaskduetotheinherentcomplexityofthehydrologicalsystemandthepresenceofvariousdynamicfactors.Traditionalmethods,suchasstatisticalmodels,havebeenemployedwithlimitedsuccessincapturingthenon-linearandtime-varyingcharacteristicsofgroundwatersystems.Inrecentyears,machinelearningtechniques,particularlyneuralnetworks,haveemergedasapowerfultoolfornonlinearregressionandpredictiontasks.Thispaperaimstoexplorethepotentialofneuralnetworksinmodelingandpredictinggroundwaterlevels.
Methodology:
Theproposedmethodologyconsistsofthreemainstages:datapreprocessing,modeltraining,andprediction.Inthedatapreprocessingstage,thecollectedgroundwaterleveldataiscarefullycleaned,filtered,andnormalizedtoensurethereliabilityandconsistencyoftheinputdataset.Missingvaluesoroutliersareimputedorremovedusingsuitabletechniques.
Inthemodeltrainingstage,afeed-forwardneuralnetworkarchitectureisemployed.Thechoiceofthenetworkarchitecturedependsonthecomplexityoftheproblem.Theinputlayerofthenetworkconsistsofvariousinfluencingfactorssuchasprecipitation,temperature,evapotranspiration,groundwaterrecharge,andlandusecharacteristics.Thesefactorsareselectedbasedontheirknowninfluenceongroundwaterlevelsanddomainknowledge.
Thehiddenlayersofthenetworkcapturethenonlinearrelationshipsbetweentheinputfactorsandtheoutputgroundwaterlevels.Thenumberofhiddenlayersandneuronsareexperimentallydeterminedtooptimizetheperformanceofthe