一个集成的cyberGIS和机器学习框架,用于使用卫星遥感和城市传感器网络数据对城市热岛进行细度预测
Fangzheng Lyu1,2, Shaohua Wang3,4, Su Yeon Han1,2
1cyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL USA.
Urban informatics
|July 31, 2023
概括
这项研究使用cyberGIS框架和机器学习预测了芝加哥的城市热岛 (UHI) 效应. 随机森林回归模型通过高频传感器数据准确预测UHI,改善城市气候预测.
科学领域:
- 环境科学 环境科学
- 地理信息科学 地理信息科学
- 城市规划 城市规划
背景情况:
- 城市热岛 (UHI) 效应加剧了气候变化和城市化的负面影响.
- 卫星数据的有限时间分辨率限制了准确的UHI预测模型.
- 高频城市传感器网络为改善UHI监控提供了潜力.
研究的目的:
- 开发一个基于网络的地理信息科学和系统 (cyberGIS) 框架,用于预测UHI.
- 将高频城市传感器网络数据与远程传感数据集成,以提高UHI预测.
- 在芝加哥的UHI预测中实现精细的时空细粒度.
主要方法:
- 开发了一个集成多个机器学习模型的cyberGIS框架.
- 利用了物联网 (AoT) 城市传感器网络和Landsat-8图像 (2018-2020) 的数据.
- 采用随机森林回归 (RFR) 进行UHI预测,并使用平均绝对误差评估性能.
主要成果:
- RFR模型显示出高预测准确度 (2020年0.45°C,2018-2019年0.8°C).
- 影响UHI预测的关键因素包括湿度,距离地理中心的距离和PM2.5度.
- 在芝加哥成功估计了10分钟的时间和1公里空间分辨率的UHI在一个高温天.
结论:
- 网络GIS框架有效地预测了UHI在精细的时空尺度上.
- 高频传感器数据和卫星图像的整合显著提高了UHI预测能力.
- 这种方法为城市规划和气候变化适应战略提供了宝贵的见解.
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