An explainable and transferable deep learning framework for spatiotemporal urban flood prediction by integrating Vision Transformer and U-Net
Key Points
Accurate urban flood prediction provides critical insights for planning and response efforts, enhancing resilience to climate impacts.
Results indicate a significant increase in predictive performance over traditional models, crucial for timely interventions.
Framework integrates deep learning with advanced architectures—specifically Vision Transformer and U-Net—for robust analysis.
The approach supports adaptability, yet further validation in diverse urban settings is needed for broader applicability.
Like
Bookmark
Share
Like
Bookmark
Share
An explainable and transferable deep learning framework for spatiotemporal urban flood prediction by integrating Vision Transformer and U-Net | Synapse