A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-Level Wind Environment Based on Deep Learning

Efficient and accurate assessment of the Pedestrian-Level Wind Environment is essential to maintain a healthy and safe urban living environment. Numerical simulations, such as computational fluid dynamics and multi-scale modeling techniques, are commonly used for wind environment analysis. However, they are computationally intensive and time-consuming, particularly when dealing with the complexities of urban landscapes. This study proposes a novel Multi-Factor-Fusion (MFF) framework that leverages deep learning techniques. This framework integrates Graph Convolutional Networks and Long Short-Term Memory networks to extract and fuse multiple factors and create an end-to-end neural network model capable of directly predicting wind fields. By avoiding the need for grid division and iterative calculations, the framework significantly enhances the efficiency of wind environment analysis. Furthermore, multi-scale simulation data is used to train the model and correct the predictive results, ensuring the accuracy of the final results. This innovative approach has the potential to revolutionize the Pedestrian-Level Wind Environment prediction by achieving a trade-off between efficiency and accuracy.