用机器学习为德黑兰城市绿地建设选优先区，提升降温效果。
Optimizing Urban Critical Green Space Development Using Machine Learning
- 结合多源数据训练随机森林模型，精准识别缺绿区域。
- 模型准确率超94%，夜间地表温度与敏感人群是关键影响因素。
- 可帮规划者定位需建绿色屋顶的降温关键区，适合智慧城市项目。
本文提出一种新框架，用于在德黑兰优先规划城市绿地发展。该框架整合了社会经济、环境及敏感性指数，数据来自Google Earth Engine、空气质量监测、市政报告和气象模型WRF。WRF模型以1公里分辨率估算气温，均方根误差（RMSE）为0.96°C，平均绝对误差（MAE）为0.92°C。数据预处理后，采用XGBoost、LightGBM、随机森林（RF）和额外树模型进行二分类植被覆盖识别，其中随机森林表现最优，总体准确率、召回率和F1分数均超过94%。随后，基于社会经济、环境与敏感性指数评估无植被覆盖区域的概率，生成绿地发展优先级地图。特征重要性分析显示，夜间地表温度（LST）和敏感人群影响最大。通过微气候模拟验证框架效果，结果显示在关键区域部署绿色屋顶后，气温最高可降低0.67°C。该框架为城市规划者提供有效工具。
原文摘要 · Abstract (English)
This paper presents a novel framework for prioritizing urban green space development in Tehran using diverse socio-economic, environmental, and sensitivity indices. The indices were derived from various sources including Google Earth Engine, air pollution measurements, municipal reports and the Weather Research & Forecasting (WRF) model. The WRF model was used to estimate the air temperature at a 1 km resolution due to insufficient meteorological stations, yielding RMSE and MAE values of 0.96°C and 0.92°C, respectively. After data preparation, several machine learning models were used for binary vegetation cover classification including XGBoost, LightGBM, Random Forest (RF) and Extra Trees. RF achieved the highest performance, exceeding 94% in Overall Accuracy, Recall, and F1-score. Then, the probability of areas lacking vegetation cover was assessed using socio-economic, environmental and sensitivity indices. This resulted in the RF generating an urban green space development prioritization map. Feature Importance Analysis revealed that the most significant indices were nightly land surface temperature (LST) and sensitive population. Finally, the framework performance was validated through microclimate simulation to assess the critical areas after and before the green space development by green roofs. The simulation demonstrated reducing air temperature by up to 0.67°C after utilizing the green roof technology in critical areas. As a result, this framework provides a valuable tool for urban planners to develop green spaces.
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