可解释的AI分析用于雾评级预测
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
概括
这项研究使用机器学习来预测个别车辆的雾贡献,达到86%的准确性. 开发的模型提供了一种评估车辆对空气质量影响的新方法.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 烟雾对人类健康和环境产生重大影响.
- 车辆是造成雾形成的主要集体贡献者.
- 量化个体汽车雾影响是具有挑战性的,但至关重要的.
研究的目的:
- 开发一种机器学习模型,用于预测个别车辆的雾贡献.
- 根据其雾影响对车辆进行分类,使用1-8级评分表.
- 利用可解释的人工智能,对汽车排放产生可操作的见解.
主要方法:
- 使用了一个数据集,包括车辆型号,年份,城市燃料消耗和燃料类型.
- 采用随机森林和可解释的提升分类器模型.
- 应用SMOTE (合成少数超样本技术) 进行数据平衡.
主要成果:
- 在预测车辆雾贡献方面取得了86%的准确性.
- 报告的平均平方误差为0.2269和R平方误差为0.9624.
- 纳入可解释的AI技术,以实现模型的可解释性.
结论:
- 拟议的机器学习方法有效地预测了车辆雾的影响.
- 结果优于之前的研究,提供及时和相关的见解.
- 这项研究是减轻车辆相关空气污染的重要一步.
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