PM2.5 concentration prediction model based on random forest regression analysis

Xu DU; Jingyu FENG; Shaoqing LV; Wei SHI

doi:10.11959/j.issn.1000−0801.2017211

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PM2.5 concentration prediction model based on random forest regression analysis

research and development | 更新时间：2024-06-05

- PM2.5 concentration prediction model based on random forest regression analysis
- Telecommunications Science Vol. 33, Issue 7, Pages: 66-75(2017)
- 作者机构：
  
  1. 西安邮电大学通信与信息工程学院，陕西西安 710121
  2. 西安邮电大学陕西省信息通信网络及安全重点实验室，陕西西安 710121
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000−0801.2017211
  CLC： TP391
- Published Online：2017-07，
  
  Published：20 July 2017
- 稿件说明：
移动端阅览
Xu DU, Jingyu FENG, Shaoqing LV, et al. PM2.5 concentration prediction model based on random forest regression analysis[J]. Telecommunications science, 2017, 33(7): 66-75.
DOI：

Xu DU, Jingyu FENG, Shaoqing LV, et al. PM2.5 concentration prediction model based on random forest regression analysis[J]. Telecommunications science, 2017, 33(7): 66-75. DOI： 10.11959/j.issn.1000−0801.2017211.

摘要

针对神经网络算法在当前PM2.5浓度预测领域存在的易过拟合、网络结构复杂、学习效率低等问题，引入RFR（random forest regression，随机森林回归）算法，分析气象条件、大气污染物浓度和季节所包含的22项特征因素，通过调整参数的最优组合，设计出一种新的PM2.5浓度预测模型——RFRP模型。同时，收集了西安市2013—2016年的历史气象数据，进行模型的有效性实验分析。实验结果表明，RFRP模型不仅能有效预测PM2.5浓度，还能在不影响预测精度的同时，较好地提升模型的运行效率，其平均运行时间为0.281 s，约为BP-NN（back propagation neural network，BP神经网络）预测模型的5.88%。

Abstract

The random foreat regression algorithm was introduced to solve the shortcomings of neural network in predicting the PM2.5 concentration

such as over-fitting

complex network structure

low learning efficiency.A novel PM2.5 concentration prediction model named RFRP was designed by analyzing the 22 characteristic factors including the meteorological conditions

the concentration of air pollutants and the season.The historical meteorological data of Xi’an in 2013—2016 were collected to verify the effectiveness of the model.The experimental results show that the proposed model can not only predict the PM2.5 concentration effectively

but also improve the operating efficiency of the model without affecting the prediction accuracy.The average run time of the proposed model is 0.281 s

which is about 5.58% of the neural network prediction model.

关键词

Keywords

references

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