Column-generation PM<sub>2.5</sub> prediction based on image mixture kernel

LI Xiao-li; ZHANG Bo; YANG Xu

doi:10.13374/j.issn2095-9389.2019.07.15.002

Volume 42 Issue 7

Jul. 2020

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Article Navigation > Chinese Journal of Engineering > 2020 > 42(7): 922-929

LI Xiao-li, ZHANG Bo, YANG Xu. Column-generation PM2.5 prediction based on image mixture kernel[J]. Chinese Journal of Engineering, 2020, 42(7): 922-929. doi: 10.13374/j.issn2095-9389.2019.07.15.002

Citation:

LI Xiao-li, ZHANG Bo, YANG Xu. Column-generation PM_2.5 prediction based on image mixture kernel[J]. Chinese Journal of Engineering, 2020, 42(7): 922-929. doi: 10.13374/j.issn2095-9389.2019.07.15.002

LI Xiao-li, ZHANG Bo, YANG Xu. Column-generation PM2.5 prediction based on image mixture kernel[J]. Chinese Journal of Engineering, 2020, 42(7): 922-929. doi: 10.13374/j.issn2095-9389.2019.07.15.002

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Column-generation PM_2.5 prediction based on image mixture kernel

doi: 10.13374/j.issn2095-9389.2019.07.15.002

LI Xiao-li^{1, 2, 3, 4},
ZHANG Bo¹,
YANG Xu^{5
,
,}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
3.
Engineering Research Center of Digital Community, Beijing 100124, China
4.
Beijing Future Network Science and Technology Innovation Center, Beijing 100124, China
5.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: yangxu@ustb.edu.cn
Received Date: 2019-07-15
Publish Date: 2020-07-01

Abstract

Abstract

The conventional method of PM_2.5 prediction requires high-precision instruments to obtain data on the concentration of pollutants, resulting in a high prediction costs. In this work, we attempt to use image data to estimate PM_2.5 concentration. The concentration of atmospheric PM_2.5 is closely linked to the image’s dark channel intensity, contrast, and color difference of HSI. The increase in atmospheric PM_2.5 concentration leads to a decrease in the non-sky area dark channel intensity, image contrast, and HSI spatial color difference. In this paper, a Column-Generation PM_2.5 prediction model based on image mixture kernel was proposed by analyzing the relationship between PM_2.5 and image features. First, the sampling period was taken as 1 h, and 8:00–17:00 was taken as the sampling range daily. The scene images were recorded in different weather conditions, and five image features were extracted, including contrast, dark channel intensity, and HSI color difference. Secondly, the image data has the characteristics of large sample size and uneven distribution, and the prediction model consists of a single kernel function, which makes it difficult to meet the prediction accuracy requirement. Therefore, the linear kernel function, polynomial kernel function, and Gauss kernel function were chosen to construct a composite model according to the concept of kernel structure from simple to complex. Then each kernel's Gram matrix was calculated based on training samples, and all gram matrices were placed into a mixture kernel matrix. Using the column generation algorithm and mixture kernel matrix, the prediction model was developed and the parameters of the model were solved. Finally, simulation experiments were performed; the results show that the prediction model based on the image mixture kernel of Column-Generation PM_2.5 can meet the prediction accuracy requirements. The model has higher prediction accuracy and better model stability in comparison with the single-kernel prediction model. A computational complexity analysis shows that the prediction model based on the image mixture kernel of column-generation PM_2.5 has no significant increase in computational complexity in comparison with the one-kernel prediction model.
- PM_2.5 prediction,
- mixed kernel function,
- column generation algorithm,
- image feature,
- prediction model

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References(21)

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