Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification
Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Trainin...
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MDPI AG
2025-03-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/3/303 |
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| author | Afaq Khattak Badr T. Alsulami Caroline Mongina Matara |
| author_facet | Afaq Khattak Badr T. Alsulami Caroline Mongina Matara |
| author_sort | Afaq Khattak |
| collection | DOAJ |
| description | Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter optimization, using data from Open-Seneca sensors along the Nairobi Expressway, combined with meteorological and traffic data. First, analysis showed that the PBT-tuned ResNet34 was the most effective model, achieving a precision (0.988), recall (0.971), F1-Score (0.979), Matthews Correlation Coefficient (MCC) of 0.904, Geometric Mean (G-Mean) of 0.962, and Balanced Accuracy (BA) of 0.962, outperforming alternative models, including ResNet18, ResNet34, and baseline approaches such as Feedforward Neural Networks (FNN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Gene Expression Programming (GEP). Subsequent feature importance analysis using a permutation-based strategy, along with SHAP analysis, revealed that humidity and hourly traffic volume were the most influential features. The findings indicated that medium to high humidity values were associated with an increased likelihood of HE, while medium to high traffic volumes similarly contributed to the occurrence of HE. |
| format | Article |
| id | doaj-art-bb02d97c612a4c8cb4472dac44a1a866 |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-bb02d97c612a4c8cb4472dac44a1a8662025-08-20T02:11:15ZengMDPI AGAtmosphere2073-44332025-03-0116330310.3390/atmos16030303Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration ClassificationAfaq Khattak0Badr T. Alsulami1Caroline Mongina Matara2Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02PN40 Dublin, IrelandCivil Engineering Department, College of Engineering and Architecture, Umm Al-Qura University, Makkah 24381, Saudi ArabiaCivil Engineering Department, Technical University of Nairobi, Nairobi P.O. Box 30197-00100, KenyaTraffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter optimization, using data from Open-Seneca sensors along the Nairobi Expressway, combined with meteorological and traffic data. First, analysis showed that the PBT-tuned ResNet34 was the most effective model, achieving a precision (0.988), recall (0.971), F1-Score (0.979), Matthews Correlation Coefficient (MCC) of 0.904, Geometric Mean (G-Mean) of 0.962, and Balanced Accuracy (BA) of 0.962, outperforming alternative models, including ResNet18, ResNet34, and baseline approaches such as Feedforward Neural Networks (FNN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Gene Expression Programming (GEP). Subsequent feature importance analysis using a permutation-based strategy, along with SHAP analysis, revealed that humidity and hourly traffic volume were the most influential features. The findings indicated that medium to high humidity values were associated with an increased likelihood of HE, while medium to high traffic volumes similarly contributed to the occurrence of HE.https://www.mdpi.com/2073-4433/16/3/303air qualityPM2.5ResNetpopulation-based training |
| spellingShingle | Afaq Khattak Badr T. Alsulami Caroline Mongina Matara Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification Atmosphere air quality PM2.5 ResNet population-based training |
| title | Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification |
| title_full | Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification |
| title_fullStr | Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification |
| title_full_unstemmed | Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification |
| title_short | Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification |
| title_sort | hybrid population based training resnet framework for traffic related pm2 5 concentration classification |
| topic | air quality PM2.5 ResNet population-based training |
| url | https://www.mdpi.com/2073-4433/16/3/303 |
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