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Among air pollutants, Particulate Matter with a diameter of less than $$2.5 \\mu m$$<\/jats:tex-math>\n \n 2.5<\/mml:mn>\n \u03bc<\/mml:mi>\n m<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> ($$PM_{2.5}$$<\/jats:tex-math>\n \n P<\/mml:mi>\n \n M<\/mml:mi>\n \n 2.5<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $$PM_{2.5}$$<\/jats:tex-math>\n \n P<\/mml:mi>\n \n M<\/mml:mi>\n \n 2.5<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $$PM_{2.5}$$<\/jats:tex-math>\n \n P<\/mml:mi>\n \n M<\/mml:mi>\n \n 2.5<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $$PM_{2.5}$$<\/jats:tex-math>\n \n P<\/mml:mi>\n \n M<\/mml:mi>\n \n 2.5<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $$PM_{2.5}$$<\/jats:tex-math>\n \n P<\/mml:mi>\n \n M<\/mml:mi>\n \n 2.5<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method \u201chybrid CNN-LSTM multivariate\u201d enables more accurate predictions than all the listed traditional models and performs better in predictive performance.<\/jats:p>","DOI":"10.1186\/s40537-021-00548-1","type":"journal-article","created":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T14:02:43Z","timestamp":1640181763000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":239,"title":["Air-pollution prediction in smart city, deep learning approach"],"prefix":"10.1186","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8373-0038","authenticated-orcid":false,"given":"Abdellatif","family":"Bekkar","sequence":"first","affiliation":[]},{"given":"Badr","family":"Hssina","sequence":"additional","affiliation":[]},{"given":"Samira","family":"Douzi","sequence":"additional","affiliation":[]},{"given":"Khadija","family":"Douzi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"548_CR1","unstructured":"Urban population (% of total population). https:\/\/data.worldbank.org\/indicator\/SP.URB.TOTL.IN.ZS Accessed 20 Oct 2021."},{"key":"548_CR2","unstructured":"Department of Economic and Social Affairs: Urban Population Change; 2018. https:\/\/www.un.org\/development\/desa\/en\/news\/population\/2018-revision-of-world-urbanization-prospects.html. 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