Yongmei Zhang1, *, Jianzhe Ma2, Lei Hu3, Keming Yu4, Lihua Song1, 5, Huini Chen1
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1929-1944, 2020, DOI:10.32604/cmc.2020.010556
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… More >
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