Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning
Air pollution consists of harmful gases and fine Particulate Matter (PM2.5) which affect the quality of air. This has not only become the key issues in scientific research but also turned to be an important social issues of the public’s life. Therefore, many experts and scholars at different R&D...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2022/5086622 |
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| author | D. Kothandaraman N. Praveena K. Varadarajkumar B. Madhav Rao Dharmesh Dhabliya Shivaprasad Satla Worku Abera |
| author_facet | D. Kothandaraman N. Praveena K. Varadarajkumar B. Madhav Rao Dharmesh Dhabliya Shivaprasad Satla Worku Abera |
| author_sort | D. Kothandaraman |
| collection | DOAJ |
| description | Air pollution consists of harmful gases and fine Particulate Matter (PM2.5) which affect the quality of air. This has not only become the key issues in scientific research but also turned to be an important social issues of the public’s life. Therefore, many experts and scholars at different R&Ds, universities, and abroad are involved in lot of research on PM2.5 pollutant predictions. In this scenario, the authors proposed various machine learning models such as linear regression, random forest, KNN, ridge and lasso, XGBoost, and AdaBoost models to predict PM2.5 pollutants in polluted cities. This experiment is carried out using Jupyter Notebook in Python 3.7.3. From the results with respect to MAE, MAPE, and RMSE metrics, among the models, XGBoost, AdaBoost, random forest, and KNN models (8.27, 0.40, and 13.85; 9.23, 0.45, and 10.59; 39.84, 1.94, and 54.59; and 49.13, 2.40, and 69.92, respectively) are observed to be more reliable models. The PM2.5 pollutant concentration (PClow-PChigh) range observed for these models is 0-18.583 μg/m3, 18.583-25.023 μg/m3, 25.023-28.234μg/m3, and 28.234-49.032 μg/m3, respectively, so these models can both predict the PM2.5 pollutant and can forecast the air quality levels in a better way. On comparison between various existing models and proposed models, it was observed that the proposed models can predict the PM2.5 pollutant with a better performance with a reduced error rate than the existing models. |
| format | Article |
| id | doaj-art-ad9d064b85d04c08925b2c0eb89a78c5 |
| institution | Kabale University |
| issn | 2048-4038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj-art-ad9d064b85d04c08925b2c0eb89a78c52025-01-02T23:44:57ZengSAGE PublishingAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/5086622Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine LearningD. Kothandaraman0N. Praveena1K. Varadarajkumar2B. Madhav Rao3Dharmesh Dhabliya4Shivaprasad Satla5Worku Abera6School of Computer Science and Artificial IntelligenceDepartment of Information TechnologyDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Computer EngineeringDepartment of Computer Science and EngineeringDepartment of Food Process EngineeringAir pollution consists of harmful gases and fine Particulate Matter (PM2.5) which affect the quality of air. This has not only become the key issues in scientific research but also turned to be an important social issues of the public’s life. Therefore, many experts and scholars at different R&Ds, universities, and abroad are involved in lot of research on PM2.5 pollutant predictions. In this scenario, the authors proposed various machine learning models such as linear regression, random forest, KNN, ridge and lasso, XGBoost, and AdaBoost models to predict PM2.5 pollutants in polluted cities. This experiment is carried out using Jupyter Notebook in Python 3.7.3. From the results with respect to MAE, MAPE, and RMSE metrics, among the models, XGBoost, AdaBoost, random forest, and KNN models (8.27, 0.40, and 13.85; 9.23, 0.45, and 10.59; 39.84, 1.94, and 54.59; and 49.13, 2.40, and 69.92, respectively) are observed to be more reliable models. The PM2.5 pollutant concentration (PClow-PChigh) range observed for these models is 0-18.583 μg/m3, 18.583-25.023 μg/m3, 25.023-28.234μg/m3, and 28.234-49.032 μg/m3, respectively, so these models can both predict the PM2.5 pollutant and can forecast the air quality levels in a better way. On comparison between various existing models and proposed models, it was observed that the proposed models can predict the PM2.5 pollutant with a better performance with a reduced error rate than the existing models.http://dx.doi.org/10.1155/2022/5086622 |
| spellingShingle | D. Kothandaraman N. Praveena K. Varadarajkumar B. Madhav Rao Dharmesh Dhabliya Shivaprasad Satla Worku Abera Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning Adsorption Science & Technology |
| title | Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning |
| title_full | Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning |
| title_fullStr | Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning |
| title_full_unstemmed | Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning |
| title_short | Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning |
| title_sort | intelligent forecasting of air quality and pollution prediction using machine learning |
| url | http://dx.doi.org/10.1155/2022/5086622 |
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