Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Benny, Nidhi; Devassy, Janita; Stephen, R.; Gobinath, R.; Siva Balan, R.V. Title A name given to the resource PM2.5 Prediction Models: A Systematic and Comparative Review Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1850 LNNS;pp.309-319 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9771-7_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9771-7_24</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030290994?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030290994?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Benny N., Christ University, Bangalore, India; Devassy J., Christ University, Bangalore, India; Stephen R., Christ University, Bangalore, India; Gobinath R., Christ University, Bangalore, India; Siva Balan R.V., Christ University, Bangalore, India Description An account of the resource Airborne particulate matter (PM) is an amalgam of liquid droplets found in air and microscopic solid particles. The particles differ in size, shape, and chemical composition. PM has a significant impact on climate and precipitation and adversely affects human health as it can infiltrate the lungs and enter the cardiovascular system. This article explores the various PM2.5 prediction models proposed to date to predict a region's particulate matter (PM2.5) concentration. As prediction techniques evolve rapidly, this study aims to assess the various methodologies proposed for predicting PM2.5 concentration in different regions according to the factors that influence it. Various machine learning, deep learning, and statistical models have been proposed to predict hourly or daily PM2.5 concentrations in the air. The previously proposed models were compared using the RMSE, MAE, and R2 scores as the evaluation metrics. Since most of these models were region-specific and mostly used different parameters for the prediction, the comparison highlighted the need for a generalized model that could be fine-tuned based on the parameters of a particular region. Thus, this review points to the need for a high-accuracy generalized prediction model for PM2.5 that adapts to the diverse parameters region-wise. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Deep learning; Machine learning; PM2.5 prediction; Time series models Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969770-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online