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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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 |
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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 |
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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 |
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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 |
13.222552 |