{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T07:06:38Z","timestamp":1792220798804,"version":"4.4.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s13198-024-02315-w","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T23:27:30Z","timestamp":1712273250000},"page":"3060-3075","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Comparative Analysis of Machine Learning Techniques in Air Quality Index (AQI) prediction in smart cities"],"prefix":"10.1007","volume":"15","author":[{"given":"Gaurav","family":"Sharma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8657-0017","authenticated-orcid":false,"given":"Savita","family":"Khurana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitin","family":"Saina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Shivansh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Garima","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"issue":"1","key":"2315_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/10630732.2014.942092","volume":"22","author":"V Albino","year":"2015","unstructured":"Albino V, Berardi U, Dangelico RM (2015) Smart cities: definitions, dimensions, performance, and initiatives. J Urban Technol 22(1):3\u201321","journal-title":"J Urban Technol"},{"issue":"7","key":"2315_CR2","doi-asserted-by":"publisher","first-page":"128325","DOI":"10.1109\/ACCESS.2019.2925082","volume":"26","author":"S Ameer","year":"2019","unstructured":"Ameer S, Shah MA, Khan A, Song H, Maple C, Islam SU, Asghar MN (2019) Comparative analysis of machine learning techniques for predicting air quality in smart cities. IEEE Access 26(7):128325\u2013128338","journal-title":"IEEE Access"},{"key":"2315_CR3","doi-asserted-by":"crossref","unstructured":"Asgari M, Farnaghi M, Ghaemi Z (2017) Predictive mapping of urban air pollution using Apache Spark on a Hadoop cluster. In: Proceedings of the 2017 international conference on cloud and big data computing 2017 Sep 17. pp 89\u201393","DOI":"10.1145\/3141128.3141131"},{"issue":"5","key":"2315_CR4","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1007\/s00521-015-1927-7","volume":"27","author":"I Bougoudis","year":"2016","unstructured":"Bougoudis I, Demertzis K, Iliadis L (2016) HISYCOL a hybrid computational intelligence system for combined machine learning:the case of air pollution modeling in Athens. Neural Comput Appl 27(5):1191\u20131206","journal-title":"Neural Comput Appl"},{"key":"2315_CR5","first-page":"2020","volume":"4","author":"M Castelli","year":"2020","unstructured":"Castelli M, Clemente FM, Popovi\u010d A, Silva S, Vanneschi L (2020) A machine learning approach to predict air quality in California. Complexity 4:2020","journal-title":"Complexity"},{"key":"2315_CR6","doi-asserted-by":"crossref","unstructured":"Cheshmehzangi A, He BJ, Sharifi A, Matzarakis A (2023) Climate change and cooling cities","DOI":"10.1007\/978-981-99-3675-5"},{"issue":"22","key":"2315_CR7","first-page":"348","volume":"1","author":"K Elass","year":"2020","unstructured":"Elass K, Eddaif A, Radey O, Aitzaouit O, Yakoubi ME, Chelhaoui Y (2020) Effect of restricted emissions during Covid-19 lockdown on air quality in Rabat-Morocco. Glob Nest J 1(22):348\u2013353","journal-title":"Glob Nest J"},{"issue":"231","key":"2315_CR8","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/j.snb.2016.03.038","volume":"1","author":"E Esposito","year":"2016","unstructured":"Esposito E, De Vito S, Salvato M, Bright V, Jones RL, Popoola O (2016) Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems. Sens Actuators, B Chem 1(231):701\u2013713","journal-title":"Sens Actuators, B Chem"},{"key":"2315_CR9","doi-asserted-by":"crossref","unstructured":"Ghoneim OA, Manjunatha BR (2017) Forecasting of ozone concentration in smart city using deep learning. In: 2017 international conference on advances in computing, communications and informatics (ICACCI) 2017 Sep 13. IEEE, pp 1320\u20131326","DOI":"10.1109\/ICACCI.2017.8126024"},{"key":"2315_CR10","doi-asserted-by":"crossref","unstructured":"Gonzalez Y, Carranza C, Iniguez M, Torres M, Quintana R, Osornio-Vargas AR, Gardner C, Sarkar S, Schwander S. Inhaled air pollution particulate matter in alveolar macrophages alters local pro-inflammatory cytokine and peripheral IFN\u0194 production in response to mycobacterium tuberculosis. In: B17. Mycobacterial host defenses 2017 May. American Thoracic Society, pp A2901\u2013A2901","DOI":"10.1016\/S2214-109X(18)30158-X"},{"key":"2315_CR11","doi-asserted-by":"crossref","unstructured":"Gore RW, Deshpande DS (2017) An approach for classification of health risks based on air quality levels. In: 2017 1st international conference on intelligent systems and information management (ICISIM) 2017 Oct 5. IEEE, pp 58\u201361","DOI":"10.1109\/ICISIM.2017.8122148"},{"key":"2315_CR12","doi-asserted-by":"crossref","unstructured":"Ho KF, Hirai HW, Kuo YH, Meng HM, Tsoi KK (2015) Indoor air monitoring platform and personal health reporting system: big data analytics for public health research. In: 2015 IEEE international congress on big data 2015 Jun 27. IEEE, pp 309\u2013312","DOI":"10.1109\/BigDataCongress.2015.51"},{"issue":"2","key":"2315_CR13","doi-asserted-by":"publisher","DOI":"10.1097\/EE9.0000000000000014","volume":"2","author":"UA Hvidtfeldt","year":"2018","unstructured":"Hvidtfeldt UA, Ketzel M, S\u00f8rensen M, Hertel O, Khan J, Brandt J, Raaschou-Nielsen O (2018) Evaluation of the Danish AirGIS air pollution modeling system against measured concentrations of PM2.5, PM10, and black carbon. Environ Epidemiol 2(2):e014","journal-title":"Environ Epidemiol"},{"issue":"12","key":"2315_CR14","first-page":"4532","volume":"8","author":"AB Ishak","year":"2017","unstructured":"Ishak AB, Daoud MB, Trabelsi A (2017) Ozone concentration forecasting using statistical learning approaches. J Mater Environ Sci 8(12):4532\u20134543","journal-title":"J Mater Environ Sci"},{"issue":"9","key":"2315_CR15","doi-asserted-by":"publisher","first-page":"3681","DOI":"10.3390\/en16093681","volume":"16","author":"H Karimi","year":"2023","unstructured":"Karimi H, Adibhesami MA, Bazazzadeh H, Movafagh S (2023) Green buildings: human-centered and energy efficiency optimization strategies. Energies 16(9):3681","journal-title":"Energies"},{"key":"2315_CR16","doi-asserted-by":"crossref","unstructured":"Keller CA, Evans MJ, Kutz JN, Pawson S (2017) Machine learning and air quality modeling. In: 2017 IEEE international conference on big data (Big Data). IEEE, pp 4570\u20134576","DOI":"10.1109\/BigData.2017.8258500"},{"issue":"7","key":"2315_CR17","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.4209\/aaqr.2016.06.0265","volume":"17","author":"A Kumar","year":"2017","unstructured":"Kumar A, Patil RS, Dikshit AK, Kumar R (2017) Application of WRF model for air quality modelling and AERMOD-a survey. Aerosol Air Qual Res 17(7):1925\u20131937","journal-title":"Aerosol Air Qual Res"},{"issue":"1","key":"2315_CR18","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3390\/atmos6010150","volume":"6","author":"Y Li","year":"2015","unstructured":"Li Y, Chen Q, Zhao H, Wang L, Tao R (2015) Variations in PM10, PM2.5 and PM1.0 in an urban area of the Sichuan Basin and their relation to meteorological factors. Atmosphere 6(1):150\u201363","journal-title":"Atmosphere"},{"issue":"22","key":"2315_CR19","doi-asserted-by":"publisher","first-page":"22408","DOI":"10.1007\/s11356-016-7812-9","volume":"23","author":"X Li","year":"2016","unstructured":"Li X, Peng L, Hu Y, Shao J, Chi T (2016) Deep learning architecture for air quality predictions. Environ Sci Pollut Res 23(22):22408\u201322417","journal-title":"Environ Sci Pollut Res"},{"key":"2315_CR20","unstructured":"Malalgoda C, Amaratunga D, Haigh R (2016) Local governments and disaster risk reduction: a conceptual framework. Massey University\/The University of Auckland"},{"issue":"1","key":"2315_CR101","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s41324-023-00541-1","volume":"32","author":"A Mishra","year":"2024","unstructured":"Mishra A, Gupta Y (2024) Comparative analysis of Air Quality Index prediction using deep learning algorithms. Spat Inf Res 32(1):63\u201372","journal-title":"Spat Inf Res"},{"issue":"2","key":"2315_CR21","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s11869-016-0414-3","volume":"10","author":"H Peng","year":"2017","unstructured":"Peng H, Lima A, Teakles A, Jin J (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual, Atmos Health 10(2):195\u2013211","journal-title":"Air Qual, Atmos Health"},{"issue":"7","key":"2315_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002602","volume":"15","author":"L Pimpin","year":"2018","unstructured":"Pimpin L, Retat L, Fecht D, de Preux L, Sassi F, Gulliver J, Belloni A, Ferguson B, Corbould E, Jaccard A, Webber L (2018) Estimating the costs of air pollution to the National Health Service and social care: an assessment and forecast up to 2035. PLoS Med 15(7):e1002602","journal-title":"PLoS Med"},{"issue":"1","key":"2315_CR23","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s41024-023-00282-6","volume":"8","author":"N Saifi","year":"2023","unstructured":"Saifi N, Belatrache D, Dadamoussa A, Settou N (2023) Effects of green roofs and vertical greenery systems on building thermal comfort in dry climates: an experimental study. J Build Pathol Rehabil 8(1):35","journal-title":"J Build Pathol Rehabil"},{"key":"2315_CR24","doi-asserted-by":"crossref","unstructured":"Yan C, Xu S, Huang Y, Huang Y, Zhang Z (2017) Two-phase neural network model for pollution concentrations forecasting. In: 2017 fifth international conference on advanced cloud and big data (CBD) 2017 Aug 13. IEEE, pp 385\u2013390","DOI":"10.1109\/CBD.2017.73"},{"issue":"8","key":"2315_CR25","doi-asserted-by":"publisher","first-page":"9089","DOI":"10.3390\/ijerph120809089","volume":"12","author":"J Wang","year":"2015","unstructured":"Wang J, Ogawa S (2015) Effects of meteorological conditions on PM2.5 concentrations in Nagasaki, Japan. Int J Environ Res Public Health 12(8):9089\u2013101","journal-title":"Int J Environ Res Public Health"},{"issue":"158","key":"2315_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2020.104814","volume":"1","author":"P Wang","year":"2020","unstructured":"Wang P, Chen K, Zhu S, Wang P, Zhang H (2020) Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour Conserv Recycl 1(158):104814","journal-title":"Resour Conserv Recycl"},{"issue":"1","key":"2315_CR27","first-page":"E69","volume":"8","author":"YF Xing","year":"2016","unstructured":"Xing YF, Xu YH, Shi MH, Lian YX (2016) The impact of PM2.5 on the human respiratory system. J Thorac Dis 8(1):E69","journal-title":"J Thorac Dis"},{"key":"2315_CR28","doi-asserted-by":"crossref","unstructured":"Yan C, Xu S, Huang Y, Huang Y, Zhang Z (2017) Two-phase neural network model for pollution concentrations forecasting. In: 2017 fifth international conference on advanced cloud and big data (CBD). IEEE, pp 385\u2013390","DOI":"10.1109\/CBD.2017.73"},{"key":"2315_CR29","doi-asserted-by":"crossref","unstructured":"Zhang X, Huang P, Sun Y (2023) The Importance of urban energy system for buildings. In Future urban energy system for buildings: the pathway towards flexibility, resilience and optimization. Springer Nature Singapore, Singapore, pp 1\u20137","DOI":"10.1007\/978-981-99-1222-3_1"},{"issue":"1","key":"2315_CR30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3390\/bdcc2010005","volume":"2","author":"D Zhu","year":"2018","unstructured":"Zhu D, Cai C, Yang T, Zhou X (2018) A machine learning approach for air quality prediction: model regularization and optimization. Big Data Cogn Comput 2(1):5","journal-title":"Big Data Cogn Comput"},{"key":"2315_CR31","doi-asserted-by":"crossref","unstructured":"Zimmerman N (2017) Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance. Atmos Meas Tech Discuss 2:1\u201336","DOI":"10.5194\/amt-2017-260"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-024-02315-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-024-02315-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-024-02315-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T09:12:41Z","timestamp":1720689161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-024-02315-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,5]]},"references-count":32,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2315"],"URL":"https:\/\/doi.org\/10.1007\/s13198-024-02315-w","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,5]]},"assertion":[{"value":"19 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest for proposed work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"The authors declare that there is no involvement of Human Participants and\/or Animals in this research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals."}},{"value":"All authors give their consent for the proposed work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}