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机器学习在大气污染研究中的应用:一篇最先进的综述
Zezhi Peng1, Bin Zhang1, Diwei Wang1
1Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
The Science of the total environment
|November 19, 2023
概括
机器学习 (ML) 通过提高预测准确性和了解污染物影响来增强大气污染研究. 在源分配和人类健康应用方面需要进一步的研究.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 为复杂的环境数据提供了强大的拟合能力.
- 大气污染研究越来越多地利用机器学习进行分析和预测.
- 对105篇文章的回顾强调了ML在这个领域日益增长的作用.
研究的目的:
- 在大气污染研究中系统审查ML应用.
- 确定改善ML预测模型的关键因素和方法.
- 探索ML在来源分配和人类健康影响评估中的作用.
主要方法:
- 关于ML和大气污染的105篇科学文章的批判性审查.
- 用于污染预测,来源分配和健康影响的ML应用的系统描述.
- 分析提高ML模型准确性和可解释性的策略.
主要成果:
- ML显著改善了大气污染物的预测,特别是颗粒物.
- 地理特征,污染物特性和优化的ML模型提高了预测准确性.
- 可解释的ML工具为模型机制和污染物行为提供了更深入的见解.
- 目前的ML应用显示出源分配和人类健康研究的局限性.
结论:
- 机器学习是大气污染研究的宝贵工具,提供了更好的预测和机制理解.
- 标准化方法和专门的ML方法对于推进源分配和人类健康研究至关重要.
- 未来的研究应该专注于整合ML,以弥合污染监测和健康结果之间的差距.
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