从数据到洞察:利用地理空间分析和可解释机器学习建模城市土地表面温度
Nhat-Duc Hoang1,2, Van-Duc Tran2,3, Thanh-Canh Huynh1,2
1Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究开发了一种机器学习模型,用于预测越南达南的陆地表面温度 (LST). 发现城市密度和绿地密度是影响LST的最重要因素.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 城市规划 城市规划
背景情况:
- 城市热岛对城市环境产生重大影响.
- 准确地对陆地表面温度 (LST) 进行建模对于了解城市热应激至关重要.
- 越南的达南面临着日益增长的城市发展和相关的热挑战.
研究的目的:
- 开发和评估用于预测城市土地表面温度 (LST) 的机器学习模型.
- 确定影响空间LST变化的关键因素.
- 为可持续城市规划和减轻热压力提供见解.
主要方法:
- 采用光梯度增强机 (LightGBM),支持向量机,随机森林和深度神经网络.
- 利用2014年,2019年和2024年的遥感数据进行模型培训和验证.
- 应用沙普利增量解释来解释模型结果并确定影响因素.
主要成果:
- 与其他基准机器学习方法相比,LightGBM表现优越.
- 城市密度和绿地密度一直被确定为影响LST的最有影响力的因素.
- 在2014年,2019年和2024年分别实现了高的R平方值 (0.85,0.92,0.91).
结论:
- 机器学习,特别是LightGBM,有效地模拟城市地区的空间LST变化.
- 城市形式特征,特别是密度和绿色空间,是LST的关键驱动因素.
- 调查结果支持基于证据的城市规划,以减轻热应激和增强城市弹性.
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