智慧农业导刊2025，Vol.5Issue(5)：9-13,5.DOI:10.20028/j.zhnydk.2025.05.003
基于集成学习的甘肃省粮食产量预测
摘要
Abstract
Aiming at the problem that the different focus points of individual prediction models lead to inaccurate grain yield prediction,the proposed method is to design a combined prediction model and select eight influencing factors:grain planting area,total power of agricultural machinery,agricultural chemical fertilizer application,disaster-stricken area,irrigated area,maxi-mum temperature,minimum temperature and sunshine hours;select and train three models:RF(random forest),gradient lifting tree GBDT and XGBoost as the base models,and use linear regression as the second level model to integrate output the final grain yield prediction results.The coefficient of determination of this stacked model is 0.98,which is greater than the coefficient of determination of a single base model.At the same time,the root-mean-square error,the mean absolute error,and the mean square error are also reduced to a minimum of 6.32,4.32 and 40.00 respectively.The results show that compared with individ-ual models,the stacked model has higher accuracy and stronger robustness for grain yield prediction.关键词
甘肃省/区域粮食安全/粮食产量预测/集成学习/影响因素Key words
Gansu Province/regional food security/grain yield prediction/integrated learning/influencing factor分类
经济学引用本文复制引用
涂丽珍,郭小燕,冯浩,赵志刚,张中铭..基于集成学习的甘肃省粮食产量预测[J].智慧农业导刊,2025,5(5):9-13,5.基金项目
甘肃农业大学大学生创新创业训练项目(202416003,202416004) （202416003,202416004）
