A library of hydrological models developed on PyTorch and designed alongside δMG for the creation of end-to-end differentiable models, enabling parameter learning, bias correction, missing process representation, and more.
See δMG/examples using hydrodl2-based HBV models for published differentiable parameter learning (dPL) applications.
This work is mantained by MHPI and advised by Dr. Chaopeng Shen. If you find it useful, please cite:
Shen, C., et al. (2023). Differentiable modelling to unify machine learning and physical models for geosciences. Nature Reviews Earth & Environment, 4(8), 552–567. <https://doi.org/10.1038/s43017-023-00450-9>.
To install hydrodl2, clone the repo and install with Astral UV (recommended):
```bash
git clone https://github.com/mhpi/hydrodl2.git
cd hydrodl2
uv pip install .
```
Optionally, add flag -e to install in editable mode.
```text
.
├── src/
| └── hydrodl2/
│ ├── api/ # Main API
│ | ├── __init__.py
│ | └── methods.py # Methods exposed to end-users
| ├── core/ # Methods used internally
│ ├── models/ # Shared models directory
│ | └── hbv/ # HBV models
| └── modules/ # Augmentations for δMG models
└── docs/
```
We welcome contributions! Please submit changes via a fork and pull requests. For more details, refer to docs/CONTRIBUTING.md.
Please submit an issue to report any questions, concerns, bugs, etc.