使用高性能深度学习模型对江苏省表面臭氧度的估计
Xi Mu1, Sichen Wang1, Peng Jiang2
1School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China.
Journal of environmental sciences (China)
|June 19, 2023
概括
一个新的深度学习模型R-ConvLSTM准确地估计了每天8小时的最大平均臭氧 (MDA8 O3) 度,捕获了关键的时空模式. 这一进步有助于了解全球臭氧水平的上升,并为环境研究提供信息.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 全球背景臭氧 (O3) 度正在上升,需要精确的监测.
- 基于地面的O3监测是可靠的,但需要高地点密度进行空间分析.
- 现有的机器学习模型往往无法完全捕获时空O3数据.
研究的目的:
- 开发一种深度学习模型,用于估计每日最大8小时平均 (MDA8) 臭氧度.
- 为了捕捉MDA8 O3在中国江苏省上空的时空特征.
- 解决现有模型在提供全面的空间和时间O3信息方面的局限性.
主要方法:
- 使用了剩余连接的卷积长短期存储器 (R-ConvLSTM) 网络.
- 综合TROPOMI总O3列数据,ERA5气象数据,以及预训练的补充数据.
- 使用剩余连接来缓解深度网络中的梯度问题.
主要成果:
- 获得了0.955的基于样本的高交叉验证R平方和9.372μg/m3的RMSE.
- 证明了基于城市的强有力的交叉验证R平方值为0.896和RMSE为14.029μg/m3.
- 确定了季节性变化,春季MDA8 O3最高 (122.60 ± 31.60 μg/m3),冬季最低 (69.93 ± 18.48 μg/m3).
结论:
- 在R-ConvLSTM模型有效估计MDA8 O3高精度和时空分辨率.
- 该模型的架构克服了梯度问题,实现了更深入的网络分析.
- 调查结果为臭氧污染动态提供了宝贵的见解,并为环境政策提供了信息.
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