Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (4): 1-7.doi: 10.6040/j.issn.1672-3961.0.2019.423

• Machine Learning & Data Mining •     Next Articles

An integrated learning approach for O3 mass concentration prediction model

Yan PENG(),Tingting FENG,Jie WANG*()   

  1. School of Management, Captial Normal University, Beijing 100048, China
  • Received:2019-07-25 Online:2020-08-20 Published:2020-08-13
  • Contact: Jie WANG E-mail:pengyan@cnu.edu.cn;wangjie@cnu.edu.cn

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Abstract:

In order to accurately predict O3 mass concentration and development trend and to analyze inducing factors, an O3 mass concentration prediction model based on integrated learning was proposed. A multilayer FS-IL model for the O3 pollutant mass concentration was established in accordance with the data of O3 pollutant mass concentration and meteorological factors from 2015 to 2016 in Beijing, on the basis of missing value filling and outlier analysis, Pearson correlation analysis and Lasso regression analysis were used to select features of the cleaned meteorological data to eliminate data redundancy and improve prediction accuracy; an integrated learning algorithm based on self-organizing featuremap (SOFM)-Elman neural network (ENN) was proposed. After clustering sample data with SOFM to realize reasonable distribution of samples, ENN was used for simulation training to predict O3 mass concentration. The experimental results showed that the accuracy of ENN-based O3 pollutant mass concentration prediction was improved from 74.6% to 82.1% after the preliminary processing of data with Pearson-Lasso feature selection and SOFM sample clustering.

Key words: Beijing, ozone, feature selection, SOFM, ENN

CLC Number: 

  • TP181

Fig.1

Structure of SOFM with one-dimensional competition layer"

Fig.2

Topology of Elman neural network"

Fig.3

Multilayer FS-IL Model"

Fig.4

Box chart of meteorological data"

Table 1

Pearson correlation number table"

Pearson相关系数 yi Pearson相关系数 yi
xi1 -0.59 xi6 0.69
xi2 -0.61 xi7 -0.09
xi3 -0.59 xi12 0.16
xi4 0.73 xi14 0.10
xi5 0.74 xi15 0.36

Table 2

Lasso feature selection coefficient"

变量 系数 变量 系数
xi1 0.0745 xi6 0.2682
xi2 -0.1147 xi7 -0.4166
xi3 0.0000 xi12 0.4466
xi4 -0.0823 xi14 0.1061
xi5 0.0415 xi15 0.1813

Fig.5

Clustering of SOFM neural network"

Fig.6

Comparison of prediction results test set of ENN"

Table 3

Comparison of simulation training results"

是否使用SOFM 使用SOFM聚类前 使用SOFM聚类后
ENN SVR RBF神经网络 BP神经网络 ENN SVR RBF神经网络 BP神经网络
MSE 0.5535 0.0649 0.5816 0.0630 0.0436 0.0451 0.4750 0.0478
R2 0.7462 0.7159 0.7318 0.7336 0.8211 0.8055 0.7932 0.8020
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