使用机器学习算法预测美国的二氧化碳排放
Bosah Philip Chukwunonso1, Ibrahim Al-Wesabi2, Li Shixiang1
1School of Public Administration, China University of Geosciences, Lumo Road 388, Wuhan, 430074, People's Republic of China.
概括
准确的二氧化碳 (CO2) 排放预测对于减缓气候变化至关重要. 一个层次循环神经网络 (L-RNN) 模型在预测美国二氧化碳排放方面表现出卓越的准确性,有助于可持续发展的政策制定.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气候变化研究 气候变化研究
背景情况:
- 人类活动造成的二氧化碳 (CO2) 排放是全球变暖和气候变化的主要驱动因素.
- 准确预测二氧化碳排放对于制定有效的减排策略和环境政策至关重要.
- 以前的努力集中在能源效率和可再生能源的采用,但精确的排放预测仍然是一个挑战.
研究的目的:
- 开发和评估用于准确预测美国二氧化碳 (CO2) 排放的机器学习算法.
- 为了比较四种不同的机器学习模型的性能:L-RNN,FFNN,CNN1和CNN2.
- 评估不同数据预处理技术对预测准确性的影响.
主要方法:
- 利用了1973年至2022年美国能源和二氧化碳排放数据集.
- 实现并比较了四种机器学习算法:层循环神经网络 (L-RNN),前神经网络 (FFNN) 和两个卷积神经网络 (CNN) 变体 (CNN1,CNN2).
- 应用了三种预处理技术:不处理,最大最小正常化和最大最小正常化与变化模式分解 (VMD) 相结合.
主要成果:
- 层级循环神经网络 (L-RNN) 模型,特别是与最大-最小规范化和VMD预处理相结合时,实现了最高的预测准确度.
- 在所有测试的模型和预处理组合中,L-RNN模型表现出最低的平均绝对百分比误差 (MAPE),平均平方误差 (MSE) 和根平均平方误差 (RMSE).
- 对2011年5月至2022年7月的二氧化碳排放量进行了预测,其中L-RNN显示出卓越的预测性能.
结论:
- L-RNN模型为预测美国二氧化碳 (CO2) 排放提供了一个高度准确和可靠的方法.
- 由L-RNN模型产生的精确的排放预测可以显著地告知减少排放的有针对性的政策的制定.
- 这项研究提高了对二氧化碳排放动态的理解,支持为可持续未来制定更有效的环境政策.
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