Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions. Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations. Deep learning, particularly generative adversarial networks (GAN), has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow. However, existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images. This study proposes a novel approach termed SFGAN, which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction. In the spatial domain, Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges, which are used as the embedded spatial characteristics. Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics. These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase, thereby leading to enhanced predictions. Experimental results demonstrate that SFGAN clearly improves wind flow prediction, reducing Wind_MAE, Wind_RMSE and the Fréchet Inception Distance (FID) score by 5.35%, 6.52% and 12.30%, compared to the previous best method, respectively. We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow. SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings. The enhanced predictions provide a better understanding of performance variability, bringing insights at the early design stage to improve pedestrian wind comfort. The proposed spatial-frequency loss term is general and can be flexibly integrated with other generative models to enhance performance with only a slight computational cost.

在日益复杂的城市条件下，行人风流是设计宜居住宅环境的关键因素。在早期设计阶段预测行人风流至关重要，但目前数值模拟效率低下。深度学习，特别是生成对抗网络（GAN），已越来越多地被采用作为有效预测行人风流的替代方法。然而，现有的基于GAN的风流预测方案由于缺乏考虑风流图像的空间和频率特征而存在局限性。本研究提出了一种称为 SFGAN 的新颖方法，该方法嵌入空间和频率特征来增强行人风流预测。在空间域中，采用高斯模糊将风流分解为包含风速和区分流边缘的分量，作为嵌入的空间特征。通过离散小波变换获得风流的详细信息，并将其作为嵌入的频率特征。风流的这些空间和频率特征被共同利用，以在训练阶段增强预测风流与地面实况之间的一致性，从而增强预测。实验结果表明，SFGAN 明显改善了风流预测，与之前的最佳方法相比，Wind_MAE、Wind_RMSE 和 Fréchet Inception Distance (FID) 分数分别降低了 5.35%、6.52% 和 12.30%。我们还分析了结合风流的空间和频率特征来预测行人风流的有效性。SFGAN 减少了大误差间隔下预测风流的误差，并且在尾流区域和建筑物周围区域表现良好。增强的预测可以更好地理解性能变异性，从而在早期设计阶段提供见解，以提高行人的风舒适度。所提出的空间频率损失项是通用的，可以灵活地与其他生成模型集成，以仅花费很少的计算成本来增强性能。