A deep analytics for prediction and forecasting of air quality in Chennai

A tétel rövid nézete
dc.contributor.authorSasikala , S
dc.contributor.authorShalini, R
dc.contributor.authorChinnapparaj , D
dc.date.accessioned2026-01-14T11:43:02Z
dc.date.available2026-01-14T11:43:02Z
dc.date.issued2025-12-31
dc.description.abstractAir pollution is a global crisis with profound implications for public health and environmental sustainability. In addressing this issue in Chennai, Tamil Nadu, a novel Hadoop-based real-time air pollution prediction system is proposed. This research offers accurate air quality information for specific Chennai regions, aiding decisions and mitigating pollution risks through big data analytics and deep learning for air quality prediction. To expedite air quality prediction, a vast air pollution dataset is rigorously analyzed using a Hadoop-based analytics model. Specific locations in Chennai, including Perungudi, Royapuram, Manali, Alandur, Arumbakkam, Kodungaiyur, and Velachery, are assessed for upto- date air quality evaluations. The core of the research revolves around implementing deep learning models—Long Short-Term Memory, Convolutional Neural Network, and a hybrid Long Short-Term Memory-Convolutional Neural Network model. These models are trained to forecast AQI for selected areas over the next five years, with the hybrid model emerging as the standout performer, achieving 99% of accuracy rate and mean absolute error, mean square error, root mean square error rates of 7.95, 101.71, 9.65. This high accuracy and low error rates empowers policymakers and environmental agencies to make informed decisions, fostering healthier living conditions in Chennai. The integration of big data analytics and deep learning, promises improved air quality management in urban areas globally, addressing similar environmental challenges and enhancing overall quality of life.en
dc.formatapplication/pdf
dc.identifier.citationActa Geographica Debrecina Landscape & Environment series, Vol. 19 No. 2 (2025) , 33-53
dc.identifier.doihttps://doi.org/10.21120/LE/19/2/3
dc.identifier.eissn1789-7556
dc.identifier.issn1789-4921
dc.identifier.issue2
dc.identifier.jatitleLandsc. environ.
dc.identifier.jtitleActa Geographica Debrecina Landscape & Environment series
dc.identifier.urihttps://hdl.handle.net/2437/402040
dc.identifier.volume19
dc.languageen
dc.relationhttps://ojs.lib.unideb.hu/landsenv/article/view/13644
dc.rights.accessOpen Access
dc.rights.ownerS Sasikala , R Shalini, D Chinnapparaj
dc.subjectair pollutionen
dc.subjectpredictionen
dc.subjectforecastingen
dc.subjectair quality indexen
dc.subjectmap reduceen
dc.subjectdeep learningen
dc.subjecthybrid modelen
dc.titleA deep analytics for prediction and forecasting of air quality in Chennaien
dc.typefolyóiratcikkhu
dc.typearticleen
dc.type.detailedidegen nyelvű folyóiratközlemény hazai lapbanhu
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Acta Geographica Debrecina Landscape & Environment (DE-journals)