基于深度学习模型的车辆CO2排放的预测,与用于可持续环境的Explainable AI集成
Gazi Mohammad Imdadul Alam1, Sharia Arfin Tanim2, Sumit Kanti Sarker2
1School of Science, Engineering & Technology, East Delta University, Chattogram, 4209, Bangladesh.
Scientific reports
|January 29, 2025
概括
本研究使用深度学习和可解释的人工智能准确预测车辆二氧化碳 (CO2) 排放,确定发动机性能和燃料消耗是减少环境影响的关键因素.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 汽车工程 汽车工程
背景情况:
- 运输部门是通过二氧化碳 (CO2) 排放对气候变化的重要贡献者.
- 汽车排放加剧了全球变暖和极端天气事件.
- 预测车辆的二氧化碳排放对于制定减排策略至关重要.
研究的目的:
- 开发和验证用于预测车辆二氧化碳排放的深度学习模型.
- 使用可解释的人工智能 (XAI) 增强模型的解释性.
- 确定影响二氧化碳排放的关键车辆属性.
主要方法:
- 利用加拿大政府的开放数据门户数据集.
- 采用深度学习,特别是一个名为CarbonMLP的多层感知器 (MLP) 架构.
- 集成式可解释的人工智能 (XAI) 方法,包括夏普利添加式扩展 (SHAP).
- 执行超参数调整以实现模型优化.
主要成果:
- 碳MLP模型实现了高精度,R平方为0.9938和MSE为0.0002.
- 高性能发动机和燃料消耗 (城市/高速公路) 被确定为对排放的重要贡献者.
- SHAP分析提供了关于特征对排放预测的重要性的见解.
- 在制造商车辆生产和燃料类型消耗趋势中确定了偏差分布.
结论:
- 拟议的深度学习和XAI方法准确预测车辆的二氧化碳排放.
- 这些发现支持制定有针对性的减排战略.
- 建议进行进一步的研究以扩大数据集,包括其他污染物,并探索现实世界的应用.
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