大理大学学报 ›› 2021, Vol. 6 ›› Issue (6): 94-100.

• 学生园地 • 上一篇    

基于组合模型对云南省昭通市空气质量指数的预测

  

  1. 大理大学数学与计算机学院,云南大理671003
  • 收稿日期:2021-02-11 出版日期:2021-06-15 发布日期:2021-06-29
  • 通讯作者: 王彭德,教授,E-mail:wpd66@126.com。
  • 作者简介:赵玉凤,2017级统计学专业本科生。
  • 基金资助:
    国家自然科学基金项目(51809026);云南省专业学位研究生教学案例库建设项目(云学位〔2019〕17号)

Prediction of Air Quality Index in Zhaotong City of Yunnan Province Based on a Combined Model

  1. College of Mathematics and Computer, Dali University, Dali, Yunnan 671003, China
  • Received:2021-02-11 Online:2021-06-15 Published:2021-06-29

摘要: 基于云南省昭通市近三年空气质量数据建立了多元线性回归、时间序列、随机森林、回归树预测模型,选择预测精度较
高的随机森林、回归树模型构建随机森林-回归树组合模型,利用标准差法确定组合模型的权重,通过对昭通市2020年8月空
气质量指数进行预测分析,发现组合模型的预测精度较单一模型的高,误差较低,因此该模型可广泛应用于空气质量的预测。

关键词: 空气质量指数, 随机森林, 回归树, 组合模型, 预测

Abstract: Based on the air quality data of Zhaotong City in Yunnan Province in the past three years, this paper establishes multiple
linear regression, time series, random forest, and regression tree prediction models, and then selects random forest and regression tree models with higher prediction accuracy to construct a combined model of random forest and regression tree, which uses the standard deviation method to determine the weights. Through the prediction and analysis of the air quality of Zhaotong City in August 2020, it is found that the combined model has higher a prediction accuracy and lower error rate than the single model. Therefore, the model can be widely used in air quality prediction.

Key words: air quality index, random forest, regression tree, combined model, prediction