二次分解在通过机器学习准确预测每日大规模臭氧污染中的重要性
Xinyue Du1, Zibing Yuan1, Daojian Huang2
1School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
The Science of the total environment
|September 11, 2023
概括
这项研究改进了臭氧污染的预测,使用了一种新的二次分解机器学习模型. 这种新方法显著提高了每天8小时平均最大臭氧 (MDA8 O3) 水平和未达到水平的预测的准确性.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 机器学习应用 机器学习应用
背景情况:
- 机器学习 (ML) 模型在预测环境污染方面是有效的,但由于复杂的影响因素,它们在每天8小时的最大平均臭氧 (MDA8 O3) 方面遇到了困难.
- 在MDA8 O3水平的不规则波动是由 synoptic 条件,局部光化学和远程传输驱动的,这给准确的预测带来了挑战.
- 准确的MDA8 O3预测对于污染的评估,管理和及时启动控制措施至关重要.
研究的目的:
- 开发和评估一种新的二次分解ML模型框架,以提高MDA8 O3预测准确度.
- 评估模型在预测O3污染未达到的效率方面的有效性,这是控制策略的关键指标.
- 调查珍珠河三角洲 (PRD) 地区预测准确度的空间变化.
主要方法:
- 开发了一种二次分解 ML 模型,将完整集体实证模式分解与自适应噪声 (CEEMDAN) 和变化模式分解 (VMD) 集成在一起.
- 在数据分解后进行预测时采用了Gate Recurrent Unit (GRU) ML模型.
- 将模型框架应用于中国珍珠河三角洲 (PRD) 的MDA8 O3预测.
主要成果:
- 二次分解ML模型显著提高了MDA8 O3预测准确度,从R2的0.46降至0.91,并将RMSE从30.4降至12.6μg/m3.
- 对O3污染的预测准确度大幅增加,从14.9%提高到72.5%.
- 由于局部污染动态,西南PRD观察到非实现的更高的预测准确性.
结论:
- 二次分解对于准确预测每日O3度和未达到的O3度至关重要.
- 开发的ML框架在O3污染预测和管理方面取得了重大进展.
- 这种方法可以扩展到全球其他地区,以加强O3预测并支持污染控制工作.
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