Article ; Online: Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam.
2023 Volume 338, Page(s) 139518
Abstract: 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 ... ...
| Abstract | 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 R |
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| MeSH term(s) | Humans ; Air Pollutants/analysis ; Cities ; Environmental Monitoring/methods ; Air Pollution/analysis ; Machine Learning ; Particulate Matter/analysis |
| Chemical Substances | Air Pollutants ; Particulate Matter |
| Language | English |
| Publishing date | 2023-07-14 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 120089-6 |
| ISSN | 1879-1298 ; 0045-6535 ; 0366-7111 |
| ISSN (online) | 1879-1298 |
| ISSN | 0045-6535 ; 0366-7111 |
| DOI | 10.1016/j.chemosphere.2023.139518 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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