量化不确定性:空气质量预测基于动态时空无噪声扩散概率模型
Kehua Chen1, Guangbo Li2, Hewen Li3
1Division of Emerging Interdisciplinary Areas (EMIA), Interdisciplinary Programs Office, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Environmental research
|February 13, 2024
概括
这项研究引入了一种新的动态空间时空排泄扩散概率模型 (DST-DDPM),用于增强空气质量预测. 该模型准确预测空气污染水平,同时量化预测不确定性和动态空间关系.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 空气污染对健康构成重大风险,需要准确的预测模型.
- 现有的模型往往忽略了预测的不确定性和动态的时空依赖性.
- 深度学习模型显示出希望,但需要在捕捉复杂关系方面取得进展.
研究的目的:
- 开发一个先进的空气质量预测模型,量化不确定性.
- 解决先前模型关于动态空间相互连接的局限性.
- 改善短期和长期空气质量预测.
主要方法:
- 提出了动态空间时空断裂扩散概率模型 (DST-DDPM).
- 利用无声扩散模型来识别预测的不确定性.
- 纳入了动态邻矩阵的动态上下文编码器,以及对依赖关系的时空否定模型.
主要成果:
- 在北京数据集上,DST-DDPM在基线模型上表现优越.
- 在短期预测中实现了1.36%的改善,在长期预测中实现了11.62%的改善.
- 在一个案例研究中成功量化了预测不确定性.
结论:
- DST-DDPM有效地捕捉空气质量数据中的动态时空依赖.
- 该模型提供了可靠的空气质量预测与量化的不确定性.
- 这种方法促进了环境监测和公共卫生保护领域的发展.
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