Articles | Volume 26, issue 19
Research article
 | Highlight paper
11 Oct 2022
Research article | Highlight paper |  | 11 Oct 2022

Improving hydrologic models for predictions and process understanding using neural ODEs

Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia

Model code and software

HydroNODE M. Höge

HydroNODE-v1.0.0 M. Höge

Executive editor
This is a paper demonstrating the added value of hybrid modeling approaches for modeling hydrological features (flow and internal states). The paper is well written and will be sent out for a detailed scientific review. The combination of ANN with ODE-based hydrological models is a good way forward to construct hybrid modeling approaches. It is a significant step forward compared to the current state of the art.
Short summary
Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.