資源描述：
《基于統(tǒng)計(jì)模型的汛期降水預(yù)測(cè)分析——以義烏市為例》由會(huì)員上傳分享，免費(fèi)在線(xiàn)閱讀，更多相關(guān)內(nèi)容在行業(yè)資料-天天文庫(kù)。
1、萬(wàn)方數(shù)據(jù)Basedonthefloodseasonprecipitationofyiwuastheresearchobject，thispaperattemptstouseneuralnetworkpredictionmethodandthresholdautoregressivemodelrespectivelytostudythefloodseasonprecipitationofyiwu．Thispaperchosetheelninoeventsandthesubtropicalhighridgelinepositionasantecedentinfluencedfac
2、torswhichhavegoodresponseoffloodseasonprecipitationofyiwu，49yearsof1959-2007floodseasonprecipitationasthetrainingsample，and4yearsoffloodseasonprecipitationof2008—201astestsamples．Thetwomodelswereanalyzedbythreeaspects：thehistoricalsamplefittingprecision，theforecastresultsofindependentsample
3、andactualforecastability．Thefinalresultisusedtoverifythepossibilitytousetheneuralnetworkmodelandthethresholdautoregressivemodelforfloodseasonprecipitationforecastinyiwu，andalsoprovidesthebasisofthetheoryandpracticeoffloodseasonprecipitationforecastmodelinthecomingyears．Themainconclusionsoft
4、hisstudyaresummarizedasfollows：(1)Thispaperstudiesthecharacteristicsoffloodseasonprecipitationofyiwu，andalsothemainfactorsinfluencingprecipitation，provedthefloodseasonprecipitationofyiwuhasagoodresponsetosolaractivity,elninoevents，andpositionofsubtropicalhighridgeline．Onthebasisofmanyresear
5、cherstostudyconclusion，thispaperchoseelninoeventsandthesubtropicalhighridgelinepositionasimpactfactorstoestablishmodels．Thefinaldataresultsconfirmedthatitcanignificantlyreducetheprecipitationforecasterrorwhileselectingtheelninoeventsandpositionofsubtropicalhighridgelineastheearlystageofthei
6、mpactfactor．(2)Neuralnetworkforecastmodelandthethresholdautoregressivemodelareeffectiveprocessingnonlinearhydrologicsystemmodeloffloodseasonprecipitationforecastofyiwucity．BPneuralnetworkforecastingmodelisbetterwhenitcomestothehistoricalsamplefittingprecision，theforecastresultsofindependent
7、sampleandactualpredictionability．However，thedataofneuralnetworkforecastmodelandthresholdpredictionmodelareinsufficient，SOtheprecisionoftheirresultsneedslmprovmg．Keywords：NeuralNetworkForecastModel；ThresholdAutoregressiveModel；FloodSeasonPrecipitation；Imp
