A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm |
| |
Authors: | Yongmei Zhang Jianzhe Ma Lei Hu Keming Yu Lihua Song Huini Chen |
| |
Affiliation: | 1.School of Information Science and Technology, North China University of Technology, Beijing, 100144, China.
2 Department of Electronic & Information Engineering, The Hong Kong Polytechnic University, 00852,
Hong Kong.
3 School of Computer Information Engineering, Jiangxi Normal University, Nanchang, 330022, China
4 Department of Mathematics, Brunel University, London, UB8 3PH, UK. 5 Department of Computer Science, George Washington University, Washington DC, 20052, USA. |
| |
Abstract: | The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in
fog and haze has been paid more and more attention, but the prediction accuracy of the
results is not ideal. Haze prediction algorithms based on traditional numerical and
statistical prediction have poor effects on nonlinear data prediction of haze. In order to
improve the effects of prediction, this paper proposes a haze feature extraction and
pollution level identification pre-warning algorithm based on feature selection and
integrated learning. Minimum Redundancy Maximum Relevance method is used to
extract low-level features of haze, and deep confidence network is utilized to extract
high-level features. eXtreme Gradient Boosting algorithm is adopted to fuse low-level
and high-level features, as well as predict haze. Establish PM2.5 concentration pollution
grade classification index, and grade the forecast data. The expert experience knowledge
is utilized to assist the optimization of the pre-warning results. The experiment results
show the presented algorithm can get better prediction effects than the results of Support
Vector Machine (SVM) and Back Propagation (BP) widely used at present, the accuracy
has greatly improved compared with SVM and BP. |
| |
Keywords: | Deep belief networks feature extraction PM2 5 eXtreme gradient boosting algorithm haze pollution |
|
| 点击此处可从《》浏览原始摘要信息 |
|
点击此处可从《》下载全文 |
|