Articles | Volume 26, issue 19
https://doi.org/10.5194/hess-26-5085-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5085-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
11 Oct 2022
Research article | Highlight paper |
| 11 Oct 2022
| 11 Oct 2022
Improving hydrologic models for predictions and process understanding using neural ODEs
Department of Systems Analysis, Integrated Assessment and Modelling, Eawag, Dübendorf, Switzerland
Andreas Scheidegger
Department of Systems Analysis, Integrated Assessment and Modelling, Eawag, Dübendorf, Switzerland
Marco Baity-Jesi
Department of Systems Analysis, Integrated Assessment and Modelling, Eawag, Dübendorf, Switzerland
Carlo Albert
Department of Systems Analysis, Integrated Assessment and Modelling, Eawag, Dübendorf, Switzerland
Fabrizio Fenicia
Department of Systems Analysis, Integrated Assessment and Modelling, Eawag, Dübendorf, Switzerland
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- Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering R. Muñoz-Carpena et al. 10.1371/journal.pwat.0000059
- Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
- Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
- Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
- Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
- A hydrological knowledge-informed LSTM model for monthly streamflow reconstruction using distributed data: Application to typical rivers across the Tibetan plateau S. Hou et al. 10.1016/j.jhydrol.2024.132409
- Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
- Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al. 10.1016/j.jhydrol.2024.132440
- CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
- Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
- Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models S. Khorram & N. Jehbez 10.1080/19942060.2024.2387051
- DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
- Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework H. Gu et al. 10.3390/w16223335
- Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow V. Shankar et al. 10.1063/5.0122115
- A fast physically-guided emulator of MATSIRO land surface model R. Olson et al. 10.1016/j.jhydrol.2024.131093
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
- Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil G. Costa et al. 10.3390/hydrology10110208
- Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning R. Palmitessa et al. 10.1016/j.watres.2022.118972
- To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
- Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
- Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method T. Amnuaylojaroen et al. 10.3390/w16223296
- A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
- Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al. 10.5194/hess-28-4521-2024
- A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
- A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India M. Saranya & V. Vinish 10.2166/wst.2023.330
- Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
- Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
- Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
- Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
Latest update: 13 Dec 2024
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.
This is a paper demonstrating the added value of hybrid modeling approaches for modeling...
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.
Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in...
