可解释的人工智能用于有效管理城市热源
Maciej Sabal1, Tomasz Danek2, Mateusz Zaręba2
1Department of Metalworking and Physical Metallurgy of Non-Ferrous Metals, Faculty of Non-Ferrous Metals, AGH University of Kraków, Kraków, Poland.
Scientific reports
|November 18, 2025
概括
由于跨境颗粒物污染,邻近的市政当局对克拉科夫的空气质量产生重大影响. 将社会经济数据纳入热源替换计划对于有效,可持续的空气质量管理至关重要.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 尽管有当地政策,但世界各地的城市仍在与空气污染作斗争,这通常是由于跨境颗粒物.
- 克拉科夫面临着严格的内部热源法规的独特挑战,但周边地区缺乏这些.
研究的目的:
- 评估邻近市镇对克拉科夫颗粒物 (PM) 水平的影响.
- 评估热源更换计划的有效性和社会经济因素对空气质量的影响.
主要方法:
- 利用时空可解释AI (XAI) 整合动态时间扭曲 (DTW) 和随机森林 (RF).
- 应用H统计和累积局部效应 (ALE) 直接可解释性.
- 纳入有关邻近市政当局的供暖来源和人均税收收入的数据.
主要成果:
- 确定了导致克拉科夫颗粒物度的排放模式.
- 证明了邻近地区对当地空气质量的重大影响.
- 强调在制定空气质量政策时需要考虑社会经济因素.
结论:
- 跨境污染严重影响城市空气质量,需要区域合作.
- 社会经济数据的整合对于开发可持续和有效的空气质量管理策略至关重要.
- 为了提高可持续性,建议对热源更换计划进行修订.
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