A multimesh graph-neural-network surrogate conditioned on discharge and trained with pushforward produces accurate 6-hour flood forecasts in 0.4 s on GPU versus 180 min on 56 CPU cores for the reference Telemac2D model.
Deep learning methods for flood mapping: a review of existing applications and future research directions
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
A multimesh graph-neural-network surrogate conditioned on discharge and trained with pushforward produces accurate 6-hour flood forecasts in 0.4 s on GPU versus 180 min on 56 CPU cores for the reference Telemac2D model.