欢迎访问林业资源管理

林业资源管理 ›› 2021, Vol. 0 ›› Issue (1): 94-102.doi: 10.13466/j.cnki.lyzygl.2021.01.013

• 科学研究 • 上一篇    下一篇

临安山核桃产量的Lasso-灰色预测模型研究

冯亚枝1,2(), 胡彦蓉1,2(), 刘洪久1,2   

  • 收稿日期:2020-11-18 修回日期:2021-01-05 出版日期:2021-02-28 发布日期:2021-03-30
  • 通讯作者: 胡彦蓉
  • 作者简介:冯亚枝(1995-),女,河南巩义人,在读硕士,从事预测与评价方面的研究。Email: fengyazhi2019@163.com
  • 基金资助:
    浙江省自然科学基金资助项目(LY17G020025);浙江省自然科学基金资助项目(LY18G010005);杭州市软科学项目(20190834M27);浙江省教育厅教育规划课题(jg20180175)

Study on Lasso-gray Prediction Model of Pecans Yield in Lin 'an

FENG Yazhi1,2(), HU Yanrong1,2(), LIU Hongjiu1,2   

  • Received:2020-11-18 Revised:2021-01-05 Online:2021-02-28 Published:2021-03-30
  • Contact: HU Yanrong

摘要:

为实现对山核桃(Carya cathayensis Sarg)产量的精确预测,分析了气候因素和种植规模对山核桃产量的影响,建立了Lasso回归和灰色预测组合模型,并以临安山核桃为例进行实证研究。结果表明,与Lasso-BP,Lasso-RBF以及Lasso-GRNN这3种模型的预测效果相比,Lasso-GM模型预测精度较高,平均相对误差达到6.99%,能够较好地掌握山核桃产量的变化规律。同时,预测结果也显示,到2024年临安山核桃产量处于稳步增长的态势。

关键词: 临安区, 山核桃, Lasso-灰色模型, 产量预测

Abstract:

In order to make accurate prediction on pecan yield,this paper takes Lin 'an pecan as an example for empirical study and analyzes the influential factors such as the climate and the planting quantity through establishing the Lasso regression and gray prediction combination model.The results show that compared with the prediction effects of the three models,Lasso-BP,Lasso-RBF and Lasso-GRNN,the prediction accuracy of the Lasso-GM model is higher,with an average relative error of 6.99 percent,from which the law of the changes of pecans output can be mastered.Meanwhile,the prediction results also show that by 2024,the output of Lin 'an pecans will continue to increase in a stable way.

Key words: Lin'an area, pecans, lasso-grey model, yield prediction

中图分类号: