Alam Gazi Mohammad Imdadul, Arfin Tanim Sharia, Sarker Sumit Kanti, Watanobe Yutaka, Islam Rashedul, Mridha M F, Nur Kamruddin
School of Science, Engineering & Technology, East Delta University, Chattogram, 4209, Bangladesh.
Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, 1229, Bangladesh.
Sci Rep. 2025 Jan 29;15(1):3655. doi: 10.1038/s41598-025-87233-y.
The transportation industry contributes significantly to climate change through carbon dioxide ( ) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing a dataset from the Canadian government's official open data portal, we explored the impact of various vehicle attributes on emissions. Our analysis reveals that not only do high-performance engines emit more pollutants, but fuel consumption under both city and highway conditions also contributes significantly to higher emissions. We identified skewed distributions in the number of vehicles produced by different manufacturers and trends in fuel consumption across fuel types. This study used deep learning techniques to construct a CO emission prediction model, specifically a light multilayer perceptron (MLP) architecture called CarbonMLP. The proposed model was optimized by hyperparameter tuning and achieved excellent performance metrics, such as a high R-squared value of 0.9938 and a low Mean Squared Error (MSE) of 0.0002. This study employs XAI approaches, particularly SHapley Additive exPlanations (SHAP), to improve the model interpretation ability and provide information about the importance of features. The findings of this study show that the proposed methodology accurately predicts CO emissions from vehicles. Additionally, the analysis suggests areas for further research, such as increasing the dataset, integrating additional pollutants, improving interpretability, and investigating real-world applications. Overall, this study contributes to the design of effective strategies for reducing vehicle CO emissions and promoting environmental sustainability.
交通运输行业通过二氧化碳排放对气候变化有重大影响，加剧全球变暖并导致更频繁、更严重的天气现象，如洪水、干旱、热浪、冰川融化和海平面上升。本研究提出了一种综合方法，利用可解释人工智能（XAI）方法增强的深度学习技术来预测车辆的二氧化碳排放。利用加拿大政府官方开放数据门户的数据集，我们探讨了各种车辆属性对二氧化碳排放的影响。我们的分析表明，不仅高性能发动机排放更多污染物，城市和高速公路条件下的燃料消耗也对更高的排放有显著贡献。我们确定了不同制造商生产的车辆数量的偏态分布以及不同燃料类型的燃料消耗趋势。本研究使用深度学习技术构建了一个二氧化碳排放预测模型，具体是一种名为CarbonMLP的轻型多层感知器（MLP）架构。所提出的模型通过超参数调整进行了优化，并取得了优异的性能指标，如高达0.9938的决定系数（R平方值）和低至0.0002的均方误差（MSE）。本研究采用XAI方法，特别是SHapley加法解释（SHAP），以提高模型的解释能力，并提供有关特征重要性的信息。本研究结果表明，所提出的方法能够准确预测车辆的二氧化碳排放。此外，分析还提出了进一步研究的领域，如增加数据集、整合其他污染物、提高可解释性以及研究实际应用。总体而言，本研究有助于设计有效的策略来减少车辆二氧化碳排放并促进环境可持续性。
