基于Stacking集成算法的中國(guó)南方地區(qū)糧食產(chǎn)量預(yù)測(cè)
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關(guān)鍵詞：Stacking集成算法；糧食產(chǎn)量；中國(guó)南方；預(yù)測(cè)
中圖分類(lèi)號(hào)：F326.11；S126 文獻(xiàn)標(biāo)識(shí)碼：A
文章編號(hào)：0439-8114（2025）05-0155-05
DOI：10.14088/j.cnki.issn0439-8114.2025.05.024 開(kāi)放科學(xué)（資源服務(wù)）標(biāo)識(shí)碼（OSID）：
Grain yield prediction in southern China based on Stacking ensemble algorithm
MADian-jing'，ZHAO Jia-song1，YAN Wei-yu1，DUANGuang-jun1，LIU Zhen-yang2，WU Shao-tian’ （1.School of Big Data，Yunnan Agricultural University，Kunming 65O2O1，China; 2.School of Data Science and Engineering，Kunming City College，Kunming 65Oo32，China）
Abstract：Basedonthe grainyielddataand11-dimensionalrelevantactorsfromAnhui，Hubei，Hunan，Jiangsu，andSichuanprov inces insoutherChinabetwen1998and2O22，theBP-SVR-Stacking grainyieldpredictionmodelbasedonthe Stacking ensemble algorithmwasdevelopedandcomparativelyanalyzedwiththeBPneuralnetwork modelandSVRmodel.Theresultsindicatedthatthe mean absolute error （ MAE ）and mean absolute percentage error （ MAPE ）of the BP-SVR-Stacking model were significantly lower than thoseof theBPneuralnetworkmodelandSVRmodel，hichdemonstratedthesuperiorpredictioncapabilityoftheBP-SVR-tacking modeloversingle machinelearningmodels.ComparedwiththeBPneuralnetworkmodelandSRmodel，thecoeficientofdetermination （ R2 ）of the BP-SVR-Stacking modelincreasedby0.124and0.122 respectively，suggesting thatthe BP-SVR-Stacking model possessed excellent fiting capability and prediction performance.
Key Words：Stacking ensemble algorithm；grain yield；southern China;prediction
糧食產(chǎn)量是一個(gè)涉及生態(tài)學(xué)、社會(huì)學(xué)、經(jīng)濟(jì)學(xué)和統(tǒng)計(jì)學(xué)的復(fù)雜問(wèn)題，其產(chǎn)量受環(huán)境、科技、經(jīng)濟(jì)、政策和勞動(dòng)力等多重因素的影響[1。（剩余7955字）