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

Clean 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...

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Main Authors: Ravindiran G., Hayder G., Kanagarathinam K., Alagumalai A., Sonne C.
Other Authors: 57226345669
Format: Article
Published: Elsevier Ltd 2024
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spelling my.uniten.dspace-340362024-10-14T11:17:44Z Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam Ravindiran G. Hayder G. Kanagarathinam K. Alagumalai A. Sonne C. 57226345669 56239664100 57203041846 56273058500 8759440300 Air quality index Climate action Gaseous pollutants Meteorological parameters Particulate matter Air Pollutants Air Pollution Cities Environmental Monitoring Humans Machine Learning Particulate Matter Andhra Pradesh India Visakhapatnam Air quality Climate models Errors Fog Forecasting Forestry Machine learning Mean square error Particles (particulate matter) Population statistics Air quality indices Air quality prediction Climate action Correlation coefficient Gaseous pollutants Machine learning models Machine-learning Meteorological parameters Particulate Matter Visakhapatnam air quality ambient air atmospheric pollution industrialization meteorology spatiotemporal analysis urban pollution air monitoring air pollution air quality Andhra Pradesh Article artificial neural network climate correlation coefficient data analysis death toll human learning algorithm machine learning mean absolute error mean squared error meteorology particulate matter root mean squared error air pollutant air pollution city environmental monitoring machine learning procedures Adaptive boosting Clean 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 Authors Final 2024-10-14T03:17:44Z 2024-10-14T03:17:44Z 2023 Article 10.1016/j.chemosphere.2023.139518 2-s2.0-85165240614 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165240614&doi=10.1016%2fj.chemosphere.2023.139518&partnerID=40&md5=164a05dd6a7249e20004967ee70e510c https://irepository.uniten.edu.my/handle/123456789/34036 338 139518 All Open Access Hybrid Gold Open Access Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Air quality index
Climate action
Gaseous pollutants
Meteorological parameters
Particulate matter
Air Pollutants
Air Pollution
Cities
Environmental Monitoring
Humans
Machine Learning
Particulate Matter
Andhra Pradesh
India
Visakhapatnam
Air quality
Climate models
Errors
Fog
Forecasting
Forestry
Machine learning
Mean square error
Particles (particulate matter)
Population statistics
Air quality indices
Air quality prediction
Climate action
Correlation coefficient
Gaseous pollutants
Machine learning models
Machine-learning
Meteorological parameters
Particulate Matter
Visakhapatnam
air quality
ambient air
atmospheric pollution
industrialization
meteorology
spatiotemporal analysis
urban pollution
air monitoring
air pollution
air quality
Andhra Pradesh
Article
artificial neural network
climate
correlation coefficient
data analysis
death toll
human
learning algorithm
machine learning
mean absolute error
mean squared error
meteorology
particulate matter
root mean squared error
air pollutant
air pollution
city
environmental monitoring
machine learning
procedures
Adaptive boosting
spellingShingle Air quality index
Climate action
Gaseous pollutants
Meteorological parameters
Particulate matter
Air Pollutants
Air Pollution
Cities
Environmental Monitoring
Humans
Machine Learning
Particulate Matter
Andhra Pradesh
India
Visakhapatnam
Air quality
Climate models
Errors
Fog
Forecasting
Forestry
Machine learning
Mean square error
Particles (particulate matter)
Population statistics
Air quality indices
Air quality prediction
Climate action
Correlation coefficient
Gaseous pollutants
Machine learning models
Machine-learning
Meteorological parameters
Particulate Matter
Visakhapatnam
air quality
ambient air
atmospheric pollution
industrialization
meteorology
spatiotemporal analysis
urban pollution
air monitoring
air pollution
air quality
Andhra Pradesh
Article
artificial neural network
climate
correlation coefficient
data analysis
death toll
human
learning algorithm
machine learning
mean absolute error
mean squared error
meteorology
particulate matter
root mean squared error
air pollutant
air pollution
city
environmental monitoring
machine learning
procedures
Adaptive boosting
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
description Clean 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 Authors
author2 57226345669
author_facet 57226345669
Ravindiran G.
Hayder G.
Kanagarathinam K.
Alagumalai A.
Sonne C.
format Article
author Ravindiran G.
Hayder G.
Kanagarathinam K.
Alagumalai A.
Sonne C.
author_sort Ravindiran G.
title Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam
title_short Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam
title_full Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam
title_fullStr Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam
title_full_unstemmed Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam
title_sort air quality prediction by machine learning models: a predictive study on the indian coastal city of visakhapatnam
publisher Elsevier Ltd
publishDate 2024
_version_ 1814061163764252672
score 13.214268