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Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam

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dc.citedby18
dc.contributor.authorRavindiran G.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorKanagarathinam K.en_US
dc.contributor.authorAlagumalai A.en_US
dc.contributor.authorSonne C.en_US
dc.contributor.authorid57226345669en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57203041846en_US
dc.contributor.authorid56273058500en_US
dc.contributor.authorid8759440300en_US
dc.date.accessioned2024-10-14T03:17:44Z
dc.date.available2024-10-14T03:17:44Z
dc.date.issued2023
dc.description.abstractClean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale. � 2023 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo139518
dc.identifier.doi10.1016/j.chemosphere.2023.139518
dc.identifier.scopus2-s2.0-85165240614
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85165240614&doi=10.1016%2fj.chemosphere.2023.139518&partnerID=40&md5=164a05dd6a7249e20004967ee70e510c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34036
dc.identifier.volume338
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofHybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleChemosphere
dc.subjectAir quality index
dc.subjectClimate action
dc.subjectGaseous pollutants
dc.subjectMeteorological parameters
dc.subjectParticulate matter
dc.subjectAir Pollutants
dc.subjectAir Pollution
dc.subjectCities
dc.subjectEnvironmental Monitoring
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectParticulate Matter
dc.subjectAndhra Pradesh
dc.subjectIndia
dc.subjectVisakhapatnam
dc.subjectAir quality
dc.subjectClimate models
dc.subjectErrors
dc.subjectFog
dc.subjectForecasting
dc.subjectForestry
dc.subjectMachine learning
dc.subjectMean square error
dc.subjectParticles (particulate matter)
dc.subjectPopulation statistics
dc.subjectAir quality indices
dc.subjectAir quality prediction
dc.subjectClimate action
dc.subjectCorrelation coefficient
dc.subjectGaseous pollutants
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectMeteorological parameters
dc.subjectParticulate Matter
dc.subjectVisakhapatnam
dc.subjectair quality
dc.subjectambient air
dc.subjectatmospheric pollution
dc.subjectindustrialization
dc.subjectmeteorology
dc.subjectspatiotemporal analysis
dc.subjecturban pollution
dc.subjectair monitoring
dc.subjectair pollution
dc.subjectair quality
dc.subjectAndhra Pradesh
dc.subjectArticle
dc.subjectartificial neural network
dc.subjectclimate
dc.subjectcorrelation coefficient
dc.subjectdata analysis
dc.subjectdeath toll
dc.subjecthuman
dc.subjectlearning algorithm
dc.subjectmachine learning
dc.subjectmean absolute error
dc.subjectmean squared error
dc.subjectmeteorology
dc.subjectparticulate matter
dc.subjectroot mean squared error
dc.subjectair pollutant
dc.subjectair pollution
dc.subjectcity
dc.subjectenvironmental monitoring
dc.subjectmachine learning
dc.subjectprocedures
dc.subjectAdaptive boosting
dc.titleAir quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnamen_US
dc.typeArticleen_US
dspace.entity.typePublication
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