This study proposes a machine learning (ML) framework generating spatially-distributed mean wind fields at a given height above ground within arbitrary urban canopy geometries. The framework is based on the Random Forest formulation and is trained using building resolving large-eddy simulations of flow over a range of realistic urban environments. The model maps up to 10 morphometric parameters, including three newly developed ones, to the mean wind over a considered horizontal plane. Predictions are computed from an ensemble of models. In independent evaluation areas, the application of the newly developed morphometric parameters increases the prediction accuracy on average by over 34 % with strengths in predicting main flow channels and areas of notably low wind speeds better than previously described morphometric parameters alone. ML-models, such as the one presented herein, are fast and efficient and are therefore suitable for operational use.

中文翻译：

---

引入新的形态测量参数以改进城市冠层气流建模：真实城市环境中的 CFD 到机器学习研究


本研究提出了一个机器学习 （ML） 框架，在任意城市树冠几何形状中，在离地面给定高度生成空间分布的平均风场。该框架基于随机森林公式，并使用建筑物解析一系列真实城市环境中水流的大涡流模拟进行训练。该模型将多达 10 个形态测量参数（包括 3 个新开发的参数）映射到所考虑的水平面上的平均风。预测是根据模型系综计算得出的。在独立评估区域，新开发的形态测量参数的应用将预测精度平均提高了 34% 以上，在预测主流通道和明显低风速的区域方面的优势比单独描述的形态测量参数要好。ML 模型，例如本文介绍的模型，快速高效，因此适合操作使用。