用集体学习,机器学习和深度学习模型为生态世界预测燃料汽车的CO2排放量
1Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Karadeniz Technical University, Trabzon, Turkey.
PeerJ. Computer science
|August 15, 2024
概括
像XGBoost和Random Forest这样的集体学习模型在预测车辆二氧化碳 (CO2) 排放方面最有效,比环境可持续性的深度学习方法提供更高的准确性和更低的错误率.
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:
- 车辆二氧化碳 (CO2) 排放量的增加导致全球变暖和气候变化.
- 准确估计和减少车辆二氧化碳排放对于环境可持续性至关重要.
- 解决温室气体排放问题是一个关键的全球性问题.
研究的目的:
- 用各种机器学习算法评估和预测燃料汽车的二氧化碳排放.
- 为了比较机器学习,集体学习和深度学习模式的性能,用于二氧化碳排放预测.
- 为准确和高效的二氧化碳排放预测确定最有效的算法.
主要方法:
- 对18种不同的回归算法进行了比较回归分析.
- 使用了来自机器学习,集体学习和深度学习的算法.
- 使用R2,调整R2,RMSE和运行时间指标来评估性能.
主要成果:
- 集体学习方法显示出更高的预测准确性和更低的错误率.
- 极端梯度提升 (XGB),随机森林和轻梯度提升机 (LGBM) 显示高R2和低RMSE.
- 深度学习模型 (CNN,DNN,GRU) 实现了高R2,但需要更多的培训时间和计算资源.
结论:
- 集体学习算法是预测车辆二氧化碳排放的最有效的算法.
- 该研究为旨在实现环境可持续性的利益相关者提供了有价值的见解.
- 研究结果支持制定减少运输产生的温室气体排放的战略.
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