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通过对机器学习,深度学习和统计模型进行比较分析,对每日CO2排放预测的检查
Adewole Adetoro Ajala1,2, Oluwatosin Lawrence Adeoye3, Olawale Moshood Salami3
1Centre of Excellence for Data Science Artificial Intelligence & Modelling (DAIM), University of Hull, HU6 7RX, Hull, United Kingdom. woltoaj@gmail.com.
Environmental science and pollution research international
|January 12, 2025
概括
准确的每日二氧化碳 (CO2) 排放预测对于短期气候目标至关重要. 机器学习和深度学习模型显著优于统计模型,由于其在预测二氧化碳排放方面的效率和准确性,建议使用集体ML方法.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 数据科学和机器学习
背景情况:
- 由于大气二氧化碳的增加,人类引起的全球变暖威胁着人类.
- 准确的每日二氧化碳排放预测对于有效的短期减排策略至关重要.
- 现有的研究往往优先考虑每年的二氧化碳排放预测,而不是每天的波动.
研究的目的:
- 评估14种不同的模型在预测每日二氧化碳排放方面的表现.
- 为了比较统计,机器学习 (ML) 和深度学习 (DL) 的方法来预测二氧化碳排放.
- 确定最佳模型,准确地预测主要污染地区的每日二氧化碳排放.
主要方法:
- 利用了中国,印度,美国,EU27和英国 (1/1/202230/9/2023) 的每日二氧化碳排放数据.
- 我们比较了4个统计模型 (ARMA,ARIMA,SARMA,SARIMA),3个ML模型 (SVM,RF,GB) 和7个DL模型 (ANN,GRU,LSTM,BILSTM,CNN-RNN混合型).
- 使用R2,MAE,RMSE和MAPE指标进行绩效评估;应用差异化和组合技术 (包装,投票).
主要成果:
- ML和DL模型显著优于统计模型,达到更高的R2 (0.714-0.932) 和较低的RMSE (0.480-0.247) 值.
- 差异化技术通过改善静态性和特征提取来提高ML和DL模型的准确性.
- 组合方法 (包装,投票) 将ML性能提高了9.6%;CNN-RNN混合物改进了RNN模型.
结论:
- 与统计方法相比,ML和DL模型在每日二氧化碳排放预测方面表现优越.
- 建议使用集体ML模型,特别是使用投票和包装,因为它们的准确性和计算效率的平衡.
- 准确的每日二氧化碳预测有助于当局制定有效的减排目标.
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