A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-level Wind Environment Based on Deep Learning
Published in IEEE Access, 2025
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, which, achieves a 10-minute prediction error of 3.52% and a 1-hour error of 10.23% in case studies.
Recommended citation: Hu, Z.Z.*, Min, Y.T., Leng, S., Li, S., Lin, J.R. (2025). A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-level Wind Environment Based on Deep Learning. IEEE Access, 13, 52912-52924. doi: 10.1109/ACCESS.2025.3553490 http://doi.org/10.1109/ACCESS.2025.3553490