Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration.
10.7507/1001-5515.201609050
- Author:
Wei WANG
1
;
Bin ZHENG
1
;
Binlin CHEN
1
;
Yaoming AN
1
;
Xiaoming JIANG
1
;
Zhangyong LI
2
Author Information
1. Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China.
2. Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China.lizy@cqupt.edu.cn.
- Publication Type:Journal Article
- Keywords:
Kalman prediction;
PM2.5 concentration;
cubic spline interpolation;
micro region
- From:
Journal of Biomedical Engineering
2018;35(1):64-69
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m , the average relative error (MER) was 6%, and the correlation coefficient was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.
