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一个可解释的机器学习框架,用于绘制热点,并确定热浪期间在城市环境中的驱动因素
Nhat-Duc Hoang1,2, Thanh-Canh Huynh3,4, Dieu-Tien Bui5
1Institute of Research and Development, Duy Tan University, 03 Quang Trung, Da Nang, 550000, Vietnam. hoangnhatduc@duytan.edu.vn.
Environmental monitoring and assessment
|August 14, 2025
概括
这项研究使用机器学习在越南达南确定了城市热点. 影响这些热点的关键因素包括建筑区,绿地和靠近水域,有助于缓解热浪的战略.
科学领域:
- 城市热岛 (UHI) 研究
- 环境科学环境科学
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 城市热岛在热浪期间存在重大风险,需要有效的缓解策略.
- 了解城市热点的驱动因素对于有针对性的干预至关重要.
研究的目的:
- 为了绘制和识别越南达南的城市热点.
- 确定影响这些热点形成的关键物理因素.
- 为城市规划者和政策制定者开发一个强大的工具.
主要方法:
- 使用极端梯度提升 (XGBoost) 进行热点分类.
- 采用沙普利添加式解释 (SHAP) 来分析因素影响.
- 分析了2024年3月至5月的Landsat 8陆地表面温度 (LST) 数据.
主要成果:
- XGBoost模型实现了高精度 (0.925),精度 (0.926),回忆 (0.923),F1得分 (0.925) 和科恩的卡帕 (0.85).
- 影响热点的五大因素:规范差异建成指数,建成密度,绿色空间密度,高度和距离水体的距离.
- 裸土指数,距离海岸线的距离,正常化差异植被指数和距离道路的距离显示出中等影响.
结论:
- 集成的XGBoost-SHAP模型有效地识别城市热点及其驱动因素.
- 调查结果为制定有针对性的城市热岛减缓战略提供了关键的见解.
- 该工具支持城市规划人员减少极端高温事件的不利影响.
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