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Hence, monitoring PM2.5 real\u2010timely becomes a key problem in environmental protection. Towards this end, this paper proposes an improved picture\u2010based prediction method of PM2.5 concentration using artificial neural network (ANN). Firstly, the weather image is transformed into Hue, Saturation, Value (HSV) color space to extract its saturation map, then the corresponding spatial and transform\u2010based entropy features of image space are extracted. Secondly, the PM2.5 concentration model is built based on the two extracted features from the weather image using Artificial Neural Network (ANN) theory. Thirdly, an ANN model is trained using the pre\u2010processed data. The training parameters and conditions are also explored through multiple experiments to achieve the best model accuracy. Experimental results show that the model has the best prediction effect when comparing to other state\u2010of\u2010the\u2010art\u00a0models.<\/jats:p>","DOI":"10.1049\/ipr2.12204","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T14:17:19Z","timestamp":1617891439000},"page":"2827-2833","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An improved picture\u2010based prediction method of PM2.5 concentration"],"prefix":"10.1049","volume":"16","author":[{"given":"Qili","family":"Chen","sequence":"first","affiliation":[{"name":"Beiijng Information Science and Technology Beijing China"}]},{"given":"Wenbai","family":"Chen","sequence":"additional","affiliation":[{"name":"Beiijng Information Science and Technology Beijing China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0115-6659","authenticated-orcid":false,"given":"Guangyuan","family":"Pan","sequence":"additional","affiliation":[{"name":"Automation and Electrical Engineering at Linyi University  Linyi China"}]}],"member":"265","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2015.04.092"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2007.05.044"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.287.9.1132"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2019.105041"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cta.2012.0749"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2498194"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2612161"},{"issue":"51","key":"e_1_2_6_9_1","first-page":"881","article-title":"The effects of fine dust, ozone, and nitrogen dioxide on health","volume":"116","author":"Beate R.","year":"2019","journal-title":"Dtsch. 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