@Article{cmc.2020.010556,
AUTHOR = {Yongmei Zhang, Jianzhe Ma, Lei Hu, Keming Yu, Lihua Song, Huini Chen},
TITLE = {A Haze Feature Extraction and Pollution Level Identification  Pre-Warning Algorithm},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1929--1944},
URL = {http://www.techscience.com/cmc/v64n3/39468},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010556}
}