Explainable AI analysis for smog rating prediction
Yazeed Yasin Ghadi1, Sheikh Muhammad Saqib2, Tehseen Mazhar3,4
1Department of Computer Science and Software Engineering, Al Ain University, 12555, Abu Dhabi, United Arab Emirates.
Scientific Reports
|March 7, 2025
Summary
This study uses machine learning to predict individual vehicle smog contributions, achieving 86% accuracy. The developed model offers a novel way to assess vehicle impact on air quality.
Area of Science:
- Environmental Science
- Computer Science
- Data Science
Background:
- Smog significantly impacts human health and the environment.
- Vehicles are a major collective contributor to smog formation.
- Quantifying individual vehicle smog impact is challenging but crucial.
Purpose of the Study:
- To develop a machine learning model for predicting individual vehicle smog contributions.
- To classify vehicles based on their smog impact using a 1-8 rating scale.
- To leverage explainable AI for actionable insights into vehicle emissions.
Main Methods:
- Utilized a dataset including vehicle model, year, city fuel consumption, and fuel type.
- Employed Random Forest and Explainable Boosting Classifier models.
- Applied SMOTE (Synthetic Minority Oversampling Technique) for data balancing.
Main Results:
- Achieved 86% accuracy in predicting vehicle smog contribution.
- Reported Mean Squared Error of 0.2269 and R-squared of 0.9624.
- Incorporated explainable AI techniques for model interpretability.
Conclusions:
- The proposed machine learning approach effectively predicts vehicle smog impact.
- Results outperform previous studies, offering timely and relevant insights.
- This research is a significant step towards mitigating vehicle-related air pollution.
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