Ep.2 AI models for flood forecasting - HydrographNet
Failed to add items
Add to basket failed.
Add to Wish List failed.
Remove from Wish List failed.
Follow podcast failed
Unfollow podcast failed
Ep.2 AI models for flood forecasting - HydrographNet
-
Narrated by:
-
By:
About this listen
This research article introduces HydroGraphNet, a novel physics-informed graph neural network for improved flood forecasting. Traditional hydrodynamic models are computationally expensive, while machine learning alternatives often lack physical accuracy and interpretability. HydroGraphNet integrates the Kolmogorov–Arnold Network (KAN) to enhance model interpretability within an unstructured mesh framework. By embedding mass conservation laws into its training and using a specific architecture, the model achieves more physically consistent and accurate predictions. Validation on real-world flood data demonstrates significant reductions in prediction error and improvements in identifying major flood events compared to standard methods.
Taghizadeh, M., Zandsalimi, Z., Nabian, M. A., Shafiee-Jood, M., & Alemazkoor, N. Interpretable physics-informed graph neural networks for flood forecasting. Computer-Aided Civil and Infrastructure Engineering. https://doi.org/10.1111/mice.13484