Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models
Vehicle-induced air pollution is an important issue in the 21st century, posing detrimental effects on human health. Prediction of vehicle-emitted air pollutants and evaluation of the diverse factors that contribute to them are of the utmost importance. This study employed advanced tree-based machin...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
D. G. Pylarinos
2024-02-01
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Series: | Engineering, Technology & Applied Science Research |
Subjects: | |
Online Access: | https://etasr.com/index.php/ETASR/article/view/6678 |
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author | Caroline Matara Simpson Osano Amir Okeyo Yusuf Elisha Ochungo Aketch |
author_facet | Caroline Matara Simpson Osano Amir Okeyo Yusuf Elisha Ochungo Aketch |
author_sort | Caroline Matara |
collection | DOAJ |
description | Vehicle-induced air pollution is an important issue in the 21st century, posing detrimental effects on human health. Prediction of vehicle-emitted air pollutants and evaluation of the diverse factors that contribute to them are of the utmost importance. This study employed advanced tree-based machine learning models to predict vehicle-induced air pollutant levels, with a particular focus on fine particulate matter (PM2.5). In addition to a benchmark statistical model, the models employed were Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), Extra Tree (ET), and Random Forest (RF). Regarding the evaluation of PM2.5 predictions, the ET model outperformed the others, as shown by MAE of 1.69, MSE of 5.91, RMSE of 2.43, and R2 of 0.71. Afterward, the optimal ET models were interpreted using SHAP analysis to overcome the ET model's lack of explainability. Based on the SHAP analysis, it was determined that temperature, humidity, and wind speed emerged as the primary determinants in forecasting PM2.5 levels.
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first_indexed | 2024-03-08T04:04:20Z |
format | Article |
id | doaj.art-13219086a58e4bed978fb09260f6a783 |
institution | Directory Open Access Journal |
issn | 2241-4487 1792-8036 |
language | English |
last_indexed | 2024-03-08T04:04:20Z |
publishDate | 2024-02-01 |
publisher | D. G. Pylarinos |
record_format | Article |
series | Engineering, Technology & Applied Science Research |
spelling | doaj.art-13219086a58e4bed978fb09260f6a7832024-02-09T06:05:56ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362024-02-0114110.48084/etasr.6678Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning ModelsCaroline Matara0Simpson Osano1Amir Okeyo Yusuf2Elisha Ochungo Aketch3Department of Civil & Construction Engineering, University of Nairobi, Kenya | School of Civil and Resource Engineering, Technical University of Kenya, KenyaDepartment of Civil & Construction Engineering, University of Nairobi, KenyaDepartment of Chemistry, University of Nairobi, KenyaDepartment of Civil, Faculty of Engineering and Technology (FoET), Multimedia University, Kenya Vehicle-induced air pollution is an important issue in the 21st century, posing detrimental effects on human health. Prediction of vehicle-emitted air pollutants and evaluation of the diverse factors that contribute to them are of the utmost importance. This study employed advanced tree-based machine learning models to predict vehicle-induced air pollutant levels, with a particular focus on fine particulate matter (PM2.5). In addition to a benchmark statistical model, the models employed were Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), Extra Tree (ET), and Random Forest (RF). Regarding the evaluation of PM2.5 predictions, the ET model outperformed the others, as shown by MAE of 1.69, MSE of 5.91, RMSE of 2.43, and R2 of 0.71. Afterward, the optimal ET models were interpreted using SHAP analysis to overcome the ET model's lack of explainability. Based on the SHAP analysis, it was determined that temperature, humidity, and wind speed emerged as the primary determinants in forecasting PM2.5 levels. https://etasr.com/index.php/ETASR/article/view/6678air pollutantsmachine learningSHAP analysis |
spellingShingle | Caroline Matara Simpson Osano Amir Okeyo Yusuf Elisha Ochungo Aketch Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models Engineering, Technology & Applied Science Research air pollutants machine learning SHAP analysis |
title | Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models |
title_full | Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models |
title_fullStr | Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models |
title_full_unstemmed | Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models |
title_short | Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models |
title_sort | prediction of vehicle induced air pollution based on advanced machine learning models |
topic | air pollutants machine learning SHAP analysis |
url | https://etasr.com/index.php/ETASR/article/view/6678 |
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