欢迎访问《农学学报》，
农学学报

农学学报 ›› 2023, Vol. 13 ›› Issue (5): 96-100.doi: 10.11923/j.issn.2095-4050.cjas2022-0052

• 农业信息 农业气象 • 上一篇    

基于岭回归的河西走廊中部日光温室低温预测模型

白青华1(), 殷雪莲1,2, 王静1, 张洁1, 褚超1, 李学军1   

  1. 1甘肃省张掖市气象局，甘肃张掖 734000
    2张掖国家气候观象台，甘肃张掖 734000
  • 收稿日期:2022-04-24 修回日期:2022-07-05 出版日期:2023-05-20 发布日期:2023-05-16
  • 作者简介:

    白青华，男，1982年出生，甘肃高台人，工程师，硕士，主要从事设施农业气象服务方面的工作。通信地址：734000 甘肃省张掖市气象局，Tel：0936-8294456，E-mail：。

  • 基金资助:
    甘肃省科技计划项目“张掖现代丝路寒旱农业气象服务技术研究”(20JR10RG823)

Prediction Model of Minimum Temperature Inside Solar Greenhouse in Central Hexi Corridor Based on Ridge Regression

BAI Qinghua1(), YIN Xuelian1,2, WANG Jing1, ZHANG Jie1, CHU Chao1, LI Xuejun1   

  1. 1Zhangye Meteorological Bureau of Gansu Province, Zhangye 734000, Gansu, China
    2Zhangye National Climate Observatory, Zhangye 734000, Gansu, China
  • Received:2022-04-24 Revised:2022-07-05 Online:2023-05-20 Published:2023-05-16

RichHTML

9

PDF (PC)

194

摘要：

为了构建基于气象要素的甘肃省甘州区日光温室低温预测模型，探索应用了岭回归分析的方法。在合理选取预测因子的基础上，对预测因子之间存在的多重共线性进行诊断，为了克服预测因子共线性对模型稳定性的影响，选择岭回归分析的方法进行共线性的处理和模型构建，应用模型的预测值与实测值对模型的精度进行检验。结果表明：选取的预测因子之间存在共线性问题，通过岭回归分析建立的日光低温预测模型可以克服预测因子间由于共线性问题对模型参数造成的影响，模型预测值与实测值之间的绝对误差(≤3℃)的准确率为98.4%，决定系数(R2)为0.8543和均方根误差(RMSE)为0.7849℃，模型精度较好。基于岭回归分析法建立的日光温室低温预测模型能够对当地日光温室低温进行合理而有效的预测。

关键词: 日光温室, 低温, 岭回归, 预测模型

Abstract:

The minimum temperature prediction model inside solar greenhouse in Ganzhou of Gansu Province was established based on meteorological elements by using ridge regression analysis. The multicollinearity of predictors was diagnosed through statistic test on the basis of reasonable selecting predictors, the ridge regression analysis was used to get over the influence of multicollinearity on the model stability, and the accuracy of the prediction model was tested by comparing simulated values and measured values. The results showed that collinearity existed among the predictors, and the prediction model of the minimum temperature inside solar greenhouse based on ridge regression could overcome the influence of collinearity on the model parameters. Between the simulated values and measured values, the accuracy rate of the absolute error (≤3℃) was 98.4%, the coefficient of determination (R2) was 0.8543, the root mean square error (RMSE) was 0.7849℃, and the accuracy of the prediction model was high. The minimum temperature prediction model based on ridge regression could reasonably and effectively predict the minimum temperature inside the local solar greenhouse.

Key words: solar greenhouse, minimum temperature, ridge regression, prediction model