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dc.contributor.authorZhang, Y-
dc.contributor.authorMa, J-
dc.contributor.authorHu, L-
dc.contributor.authorYu, K-
dc.contributor.authorSong, L-
dc.contributor.authorChen, H-
dc.identifier.citationZhang, Y., Ma, J., Hu, L., Yu, K., Song, L. and Chen, H. (2020) 'A haze feature extraction and pollution level identification pre-warning algorithm,' Computers, Materials and Continua, 64 (3), pp. 1929 - 1944. doi: 10.32604/cmc.2020.010556.en_US
dc.description.abstractCopyright © 2020 The Author(s). 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.en_US
dc.description.sponsorshipNational Natural Science Fund of China (Grant Nos. 61371143, 61662033).en_US
dc.format.extent1929 - 1944-
dc.publisherTech Science Pressen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.-
dc.subjectdeep belief networksen_US
dc.subjectfeature extractionen_US
dc.subjecteXtreme gradient boosting algorithmen_US
dc.subjecthaze pollutionen_US
dc.titleA haze feature extraction and pollution level identification pre-warning algorithmen_US
dc.relation.isPartOfComputers, Materials and Continua-
Appears in Collections:Dept of Mathematics Research Papers

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