Column-generation PM2.5 prediction based on image mixture kernel

Abstract

The conventional method of PM2.5 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 PM2.5 concentration. The concentration of atmospheric PM2.5 is closely linked to the image’s dark channel intensity, contrast, and color difference of HSI. The increase in atmospheric PM2.5 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 PM2.5 prediction model based on image mixture kernel was proposed by analyzing the relationship between PM2.5 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 PM2.5 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 PM2.5 has no significant increase in computational complexity in comparison with the one-kernel prediction model.

Keywords

• pm2.5 prediction
• mixed kernel function
• column generation algorithm
• image feature
• prediction model