图表机器学习用于改善缺失的热带层臭氧数据的推算.
Clara Betancourt1, Cathy W Y Li1,2, Felix Kleinert1
1Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany.
Environmental science & technology
|September 4, 2023
概括
解决缺少的大气污染物数据对于准确的趋势分析至关重要. 这项研究引入了一种混合图形机器学习方法,以有效地计算热层臭氧测量的差距,提高数据可靠性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气化学 大气化学
背景情况:
- 大气污染物测量的差距阻碍了影响和趋势评估.
- 准确的数据归算对于可靠的环境监测至关重要.
研究的目的:
- 开发和评估一种新的方法,用于归因失踪的热层臭氧数据.
- 通过图形机器学习提高臭氧数据归算的准确性.
主要方法:
- 利用图形机器学习算法"正确和顺"进行数据归算.
- 应用了描述测量地点和邻近臭氧观测的辅助数据.
- 在不同的间隙模式中比较性能:短 (小时),长 (月) 和多站间隙.
主要成果:
- 线性插值在短时间间隔 (长达5小时) 中是最准确的.
- 一个随机的森林与"正确和光滑"相结合,有效地在单个站点中计算出更长的间隙.
- "正确和顺"算法提高了随机森林性能,用于多站间隙,即使邻里数据有限.
结论:
- 建议在热层臭氧时间序列推算中采用混合方法,将线性插值和图形机器学习相结合.
- 拟议的方法提高了大气污染物数据分析的可靠性.
- 图形机器学习为解决环境监测网络中的数据缺口提供了一个强大的工具.
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