基于Stacking集成算法的中国南方地区粮食产量预测

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关键词：Stacking集成算法；粮食产量；中国南方；预测

中图分类号：F326.11；S126 文献标识码：A

文章编号：0439-8114（2025）05-0155-05

DOI：10.14088/j.cnki.issn0439-8114.2025.05.024 开放科学（资源服务）标识码（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

粮食产量是一个涉及生态学、社会学、经济学和统计学的复杂问题，其产量受环境、科技、经济、政策和劳动力等多重因素的影响[1。（剩余7955字）