Towards Transferable Models for Efficient Spatiotemporal Flood Prediction Across Coastal-Estuarine Systems

This perspective examines the application of transfer learning (TL) within deep learning (DL) frameworks for extreme water level (EWL) and spatiotemporal flood predictions.We discuss the main advantages of TL, such as enabling model transferability of pretrained DL models from/to diverse coastal-estuarine systems and reducing computational time compared to physics-based models.These advantages can accelerate the deployment of flood prediction models in data-limited locations.We also discuss challenges and limitations that hinder accurate pattern recognition and propagation of EWLs from gauge (observation) stations to surrounding locations within model domains.These limitations include dependence on similarity in data distributions, overfitting the training data, and both lag and hysteresis effects in the timing of peak water levels and flood dynamics.