Explainable AI analysis for smog rating prediction
Abstract Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, and ammonia from fertilizers, vehicles play a significant role. Individually, a vehicle’s...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-025-92788-x |
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| author | Yazeed Yasin Ghadi Sheikh Muhammad Saqib Tehseen Mazhar Ahmad Almogren Wajahat Waheed Ayman Altameem Habib Hamam |
| author_facet | Yazeed Yasin Ghadi Sheikh Muhammad Saqib Tehseen Mazhar Ahmad Almogren Wajahat Waheed Ayman Altameem Habib Hamam |
| author_sort | Yazeed Yasin Ghadi |
| collection | DOAJ |
| description | Abstract Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, and ammonia from fertilizers, vehicles play a significant role. Individually, a vehicle’s contribution to smog may be small, but collectively, the vast number of vehicles has a substantial impact. Manually assessing the contribution of each vehicle to smog is impractical. However, advancements in machine learning make it possible to quantify this contribution. By creating a dataset with features such as vehicle model, year, fuel consumption (city), and fuel type, a predictive model can classify vehicles based on their smog impact, rating them on a scale from 1 (poor) to 8 (excellent). This study proposes a novel approach using Random Forest and Explainable Boosting Classifier models, along with SMOTE (Synthetic Minority Oversampling Technique), to predict the smog contribution of individual vehicles. The results outperform previous studies, with the proposed model achieving an accuracy of 86%. Key performance metrics include a Mean Squared Error of 0.2269, R-Squared (R2) of 0.9624, Mean Absolute Error of 0.2104, Explained Variance Score of 0.9625, and a Max Error of 4.3500. These results incorporate explainable AI techniques, using both agnostic and specific models, to provide clear and actionable insights. This work represents a significant step forward, as the dataset was last updated only five months ago, underscoring the timeliness and relevance of the research. |
| format | Article |
| id | doaj-art-e6419542a48b4cbb85b8a862c3617f4e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-e6419542a48b4cbb85b8a862c3617f4e2025-08-20T02:59:24ZengNature PortfolioScientific Reports2045-23222025-03-0115112310.1038/s41598-025-92788-xExplainable AI analysis for smog rating predictionYazeed Yasin Ghadi0Sheikh Muhammad Saqib1Tehseen Mazhar2Ahmad Almogren3Wajahat Waheed4Ayman Altameem5Habib Hamam6Department of Computer Science and Software Engineering, Al Ain UniversityDepartment of Computing and Information Technology, Gomal UniversitySchool of Computer Science, National College of Business Administration and EconomicsDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Electrical and Computer Engineering, Purdue UniversityDepartment of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud UniversityFaculty of Engineering, Uni de MonctonAbstract Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, and ammonia from fertilizers, vehicles play a significant role. Individually, a vehicle’s contribution to smog may be small, but collectively, the vast number of vehicles has a substantial impact. Manually assessing the contribution of each vehicle to smog is impractical. However, advancements in machine learning make it possible to quantify this contribution. By creating a dataset with features such as vehicle model, year, fuel consumption (city), and fuel type, a predictive model can classify vehicles based on their smog impact, rating them on a scale from 1 (poor) to 8 (excellent). This study proposes a novel approach using Random Forest and Explainable Boosting Classifier models, along with SMOTE (Synthetic Minority Oversampling Technique), to predict the smog contribution of individual vehicles. The results outperform previous studies, with the proposed model achieving an accuracy of 86%. Key performance metrics include a Mean Squared Error of 0.2269, R-Squared (R2) of 0.9624, Mean Absolute Error of 0.2104, Explained Variance Score of 0.9625, and a Max Error of 4.3500. These results incorporate explainable AI techniques, using both agnostic and specific models, to provide clear and actionable insights. This work represents a significant step forward, as the dataset was last updated only five months ago, underscoring the timeliness and relevance of the research.https://doi.org/10.1038/s41598-025-92788-xMachine learningRandom forestExplainable boosting classifierSMOTEExplainable-AI |
| spellingShingle | Yazeed Yasin Ghadi Sheikh Muhammad Saqib Tehseen Mazhar Ahmad Almogren Wajahat Waheed Ayman Altameem Habib Hamam Explainable AI analysis for smog rating prediction Scientific Reports Machine learning Random forest Explainable boosting classifier SMOTE Explainable-AI |
| title | Explainable AI analysis for smog rating prediction |
| title_full | Explainable AI analysis for smog rating prediction |
| title_fullStr | Explainable AI analysis for smog rating prediction |
| title_full_unstemmed | Explainable AI analysis for smog rating prediction |
| title_short | Explainable AI analysis for smog rating prediction |
| title_sort | explainable ai analysis for smog rating prediction |
| topic | Machine learning Random forest Explainable boosting classifier SMOTE Explainable-AI |
| url | https://doi.org/10.1038/s41598-025-92788-x |
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