{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T16:30:27Z","timestamp":1776789027125,"version":"3.51.2"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03339-6","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T10:01:45Z","timestamp":1730800905000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Air Quality Prediction Using Machine Learning Models: A Predictive Study in the Himalayan City of Rishikesh"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4217-0182","authenticated-orcid":false,"given":"Ishaan","family":"Dawar","sequence":"first","affiliation":[]},{"given":"Maanas","family":"Singal","sequence":"additional","affiliation":[]},{"given":"Vijayant","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Sumita","family":"Lamba","sequence":"additional","affiliation":[]},{"given":"Shreyal","family":"Jain","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"3339_CR1","doi-asserted-by":"publisher","first-page":"8972","DOI":"10.3390\/ijerph17238972","volume":"17","author":"S Vardoulakis","year":"2020","unstructured":"Vardoulakis S, Giagloglou E, Steinle S, Davis A, Sleeuwenhoek A, Galea KS, et al. Indoor exposure to selected air pollutants in the home environment: a systematic review. Int J Environ Res Public Health. 2020;17:8972. https:\/\/doi.org\/10.3390\/ijerph17238972.","journal-title":"Int J Environ Res Public Health"},{"key":"3339_CR2","unstructured":"Ambient (Outdoor) Air pollution. World Health Organization (WHO). 2022. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ambient-(outdoor)-air-quality-and-health. Accessed 20 Mar 2024."},{"key":"3339_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.139052","volume":"731","author":"R Bao","year":"2020","unstructured":"Bao R, Zhang A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci Total Environ. 2020;731: 139052. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.139052.","journal-title":"Sci Total Environ"},{"key":"3339_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.dche.2023.100093","volume":"7","author":"NN Maltare","year":"2023","unstructured":"Maltare NN, Vahora S. Air Quality Index prediction using machine learning for Ahmedabad city. Digit Chem Eng. 2023;7: 100093. https:\/\/doi.org\/10.1016\/j.dche.2023.100093.","journal-title":"Digit Chem Eng"},{"key":"3339_CR5","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.3390\/su13031318","volume":"13","author":"GS Malhi","year":"2021","unstructured":"Malhi GS, Kaur M, Kaushik P. Impact of climate change on agriculture and its mitigation strategies: a review. Sustainability. 2021;13:1318. https:\/\/doi.org\/10.3390\/su13031318.","journal-title":"Sustainability"},{"key":"3339_CR6","unstructured":"Health Effects of Particulate Matter, Policy Implications for Eastern Europe, Caucasus, and Central Asia Countries. World Health Organization (WHO). 2012. https:\/\/unece.org\/fileadmin\/DAM\/env\/documents\/2012\/air\/WGE_31th\/n_1_TFH_PM_paper_on_health_effects_-_draft_for_WGE_comments.pdf. Accessed 24 Mar 2024."},{"key":"3339_CR7","doi-asserted-by":"publisher","DOI":"10.1029\/2020gl091202","volume":"48","author":"Y Rybarczyk","year":"2021","unstructured":"Rybarczyk Y, Zalakeviciute R. Assessing the COVID-19 impact on air quality: a machine learning approach. Geophys Res Lett. 2021;48: e2020GL091202. https:\/\/doi.org\/10.1029\/2020gl091202.","journal-title":"Geophys Res Lett"},{"key":"3339_CR8","doi-asserted-by":"publisher","first-page":"436","DOI":"10.5094\/apr.2011.050","volume":"2","author":"A Kumar","year":"2011","unstructured":"Kumar A, Goyal P. Forecasting of air quality in Delhi using principal component regression technique. Atmos Pollut Res. 2011;2:436\u201344. https:\/\/doi.org\/10.5094\/apr.2011.050.","journal-title":"Atmos Pollut Res"},{"key":"3339_CR9","doi-asserted-by":"publisher","unstructured":"Bhushan M, Dawar I, Sharma S, Bawaniya TK, Anand U, Negi A. Air quality prediction using machine learning and deep learning: an exploratory study. In: 2023 7th international conference on computing, communication, control and automation (ICCUBEA). IEEE; 2023. p. 1\u20136. https:\/\/doi.org\/10.1109\/ICCUBEA58933.2023.10392048.","DOI":"10.1109\/ICCUBEA58933.2023.10392048"},{"key":"3339_CR10","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1016\/j.procs.2020.04.221","volume":"171","author":"KS Harishkumar","year":"2020","unstructured":"Harishkumar KS, Yogesh KM, Gad I. Forecasting air pollution particulate matter (PM2.5) using machine learning regression models. Proc Comput Sci. 2020;171:2057\u201366. https:\/\/doi.org\/10.1016\/j.procs.2020.04.221.","journal-title":"Proc Comput Sci"},{"key":"3339_CR11","doi-asserted-by":"publisher","first-page":"8049504","DOI":"10.1155\/2020\/8049504","volume":"2020","author":"M Castelli","year":"2020","unstructured":"Castelli M, Clemente FM, Popovi\u010d A, Silva S, Vanneschi L. A machine learning approach to predict air quality in California. Complexity. 2020;2020:8049504. https:\/\/doi.org\/10.1155\/2020\/8049504.","journal-title":"Complexity"},{"key":"3339_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.136991","volume":"715","author":"S Masmoudi","year":"2020","unstructured":"Masmoudi S, Elghazel H, Taieb D, Yazar O, Kallel A. A machine-learning framework for predicting multiple air pollutants\u2019 concentrations via multi-target regression and feature selection. Sci Total Environ. 2020;715: 136991. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.136991.","journal-title":"Sci Total Environ"},{"key":"3339_CR13","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.3390\/s24051532","volume":"24","author":"Y El Mghouchi","year":"2024","unstructured":"El Mghouchi Y, Udristioiu MT, Yildizhan H. Multivariable air-quality prediction and modelling via hybrid machine learning: a case study for Craiova, Romania. Sensors. 2024;24:1532. https:\/\/doi.org\/10.3390\/s24051532.","journal-title":"Sensors"},{"key":"3339_CR14","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/s13762-023-05016-2","volume":"21","author":"SA Aram","year":"2024","unstructured":"Aram SA, Nketiah EA, Saalidong BM, Wang H, Afitiri A-R, Akoto AB, et al. Machine learning-based prediction of air quality index and air quality grade: a comparative analysis. Int J Environ Sci Technol. 2024;21:1345\u201360. https:\/\/doi.org\/10.1007\/s13762-023-05016-2.","journal-title":"Int J Environ Sci Technol"},{"key":"3339_CR15","first-page":"367","volume":"8","author":"P Bhalgat","year":"2019","unstructured":"Bhalgat P, Pitale S, Bhoite S. Air quality prediction using machine learning algorithms. Int J Comput Appl Technol Res. 2019;8:367\u201370.","journal-title":"Int J Comput Appl Technol Res"},{"key":"3339_CR16","doi-asserted-by":"publisher","unstructured":"Srivastava C, Singh S, Singh AP. Estimation of air pollution in Delhi using machine learning techniques. In: 2018 international conference on computing, power and communication technologies (GUCON). IEEE; 2018. p. 304\u20139. https:\/\/doi.org\/10.1109\/GUCON.2018.8675022.","DOI":"10.1109\/GUCON.2018.8675022"},{"key":"3339_CR17","first-page":"1621","volume":"12","author":"S Hansun","year":"2019","unstructured":"Hansun S, Kristanda MB. AQI measurement and prediction using B-wema method. Int J Eng Res Technol. 2019;12:1621\u20135.","journal-title":"Int J Eng Res Technol"},{"key":"3339_CR18","doi-asserted-by":"publisher","first-page":"128325","DOI":"10.1109\/ACCESS.2019.2925082","volume":"7","author":"S Ameer","year":"2019","unstructured":"Ameer S, Shah MA, Khan A, Song H, Maple C, Islam SU, et al. Comparative analysis of machine learning techniques for predicting air quality in smart cities. IEEE Access. 2019;7:128325\u201338. https:\/\/doi.org\/10.1109\/ACCESS.2019.2925082.","journal-title":"IEEE Access"},{"key":"3339_CR19","doi-asserted-by":"publisher","first-page":"4430","DOI":"10.1109\/JSEN.2020.2964396","volume":"20","author":"QP Ha","year":"2020","unstructured":"Ha QP, Metia S, Phung MD. Sensing data fusion for enhanced indoor air quality monitoring. IEEE Sens J. 2020;20:4430\u201341. https:\/\/doi.org\/10.1109\/JSEN.2020.2964396.","journal-title":"IEEE Sens J"},{"key":"3339_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107572","volume":"96","author":"S Ojagh","year":"2021","unstructured":"Ojagh S, Cauteruccio F, Terracina G, Liang SHL. Enhanced air quality prediction by edge-based spatiotemporal data preprocessing. Comput Electr Eng. 2021;96: 107572. https:\/\/doi.org\/10.1016\/j.compeleceng.2021.107572.","journal-title":"Comput Electr Eng"},{"key":"3339_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00548-1","volume":"8","author":"A Bekkar","year":"2021","unstructured":"Bekkar A, Hssina B, Douzi S, Douzi K. Air-pollution prediction in smart city, deep learning approach. J Big Data. 2021;8:1\u201321.","journal-title":"J Big Data"},{"key":"3339_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102720","volume":"67","author":"R Janarthanan","year":"2021","unstructured":"Janarthanan R, Partheeban P, Somasundaram K, Elamparithi PN. A deep learning approach for prediction of air quality index in a metropolitan city. Sustain Cities Soc. 2021;67: 102720. https:\/\/doi.org\/10.1016\/j.scs.2021.102720.","journal-title":"Sustain Cities Soc"},{"key":"3339_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2023.139518","volume":"338","author":"G Ravindiran","year":"2023","unstructured":"Ravindiran G, Hayder G, Kanagarathinam K, Alagumalai A, Sonne C. Air quality prediction by machine learning models: a predictive study on the indian coastal city of Visakhapatnam. Chemosphere. 2023;338: 139518. https:\/\/doi.org\/10.1016\/j.chemosphere.2023.139518.","journal-title":"Chemosphere"},{"key":"3339_CR24","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1007\/s12145-021-00618-1","volume":"14","author":"JK Sethi","year":"2021","unstructured":"Sethi JK, Mittal M. An efficient correlation based adaptive LASSO regression method for air quality index prediction. Earth Sci Inform. 2021;14:1777\u201386. https:\/\/doi.org\/10.1007\/s12145-021-00618-1.","journal-title":"Earth Sci Inform"},{"key":"3339_CR25","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1146\/annurev.psych.58.110405.085530","volume":"60","author":"JW Graham","year":"2009","unstructured":"Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549\u201376.","journal-title":"Annu Rev Psychol"},{"key":"3339_CR26","doi-asserted-by":"publisher","first-page":"887","DOI":"10.3390\/sym15040887","volume":"15","author":"W Chandra","year":"2023","unstructured":"Chandra W, Suprihatin B, Resti Y. Median-KNN Regressor-SMOTE-Tomek links for handling missing and imbalanced data in air quality prediction. Symmetry (Basel). 2023;15:887. https:\/\/doi.org\/10.3390\/sym15040887.","journal-title":"Symmetry (Basel)"},{"key":"3339_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2022.118972","volume":"301","author":"AI Middya","year":"2022","unstructured":"Middya AI, Roy S. Pollutant specific optimal deep learning and statistical model building for air quality forecasting. Environ Pollut. 2022;301: 118972. https:\/\/doi.org\/10.1016\/j.envpol.2022.118972.","journal-title":"Environ Pollut"},{"key":"3339_CR28","doi-asserted-by":"publisher","unstructured":"Sammut C, Webb GI. Encyclopedia of machine learning. Springer Science & Business Media; 2011. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_528.","DOI":"10.1007\/978-0-387-30164-8_528"},{"key":"3339_CR29","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1080\/00220970009600095","volume":"68","author":"J Nevitt","year":"2000","unstructured":"Nevitt J, Hancock GR. Improving the root mean square error of approximation for nonnormal conditions in structural equation modeling. J Exp Educ. 2000;68:251\u201368. https:\/\/doi.org\/10.1080\/00220970009600095.","journal-title":"J Exp Educ"},{"key":"3339_CR30","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.623","volume":"7","author":"D Chicco","year":"2021","unstructured":"Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci. 2021;7: e623. https:\/\/doi.org\/10.7717\/peerj-cs.623.","journal-title":"PeerJ Comput Sci"},{"key":"3339_CR31","doi-asserted-by":"publisher","first-page":"140","DOI":"10.3390\/info12040140","volume":"12","author":"T Langer","year":"2021","unstructured":"Langer T, Meisen T. System design to utilize domain expertise for visual exploratory data analysis. Information. 2021;12:140. https:\/\/doi.org\/10.3390\/info12040140.","journal-title":"Information"},{"key":"3339_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.envc.2021.100356","volume":"5","author":"DP Shah","year":"2021","unstructured":"Shah DP, Patel P. A comparison between national air quality index, india and composite air quality index for Ahmedabad, India. Environ Chall. 2021;5: 100356. https:\/\/doi.org\/10.1016\/j.envc.2021.100356.","journal-title":"Environ Chall"},{"key":"3339_CR33","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1139\/s03-047","volume":"2","author":"M Sharma","year":"2003","unstructured":"Sharma M, Pandey R, Maheshwari M, Sengupta B, Shukla BP, Gupta NK, et al. Interpretation of air quality data using an air quality index for the city of Kanpur, India. J Environ Eng Sci. 2003;2:453\u201362. https:\/\/doi.org\/10.1139\/s03-047.","journal-title":"J Environ Eng Sci"},{"key":"3339_CR34","unstructured":"Uttarakhand Environment Protection and Pollution Board D. Rishikesh City Action Plan, Dehradun. 2023. https:\/\/cpcb.nic.in\/Actionplan\/Rishikesh.pdf. Accessed 24 Mar 2024."},{"key":"3339_CR35","doi-asserted-by":"publisher","unstructured":"Chandrappa R, Chandra Kulshrestha U, Chandrappa R, Chandra Kulshrestha U. Air pollution and disasters. In: Sustainable air pollution management: theory and practice. 2016. p. 325\u201343. https:\/\/doi.org\/10.1007\/978-3-319-21596-9_8.","DOI":"10.1007\/978-3-319-21596-9_8"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03339-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03339-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03339-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T10:02:05Z","timestamp":1730800925000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03339-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,5]]},"references-count":35,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3339"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03339-6","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,5]]},"assertion":[{"value":"8 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1025"}}