hy2dl 1.0.1

Python library to create hydrological models for rainfall-runoff prediction using deep learning methods

Hy²DL: Hybrid Hydrological modeling using Deep Learning methods

Hy²DL is a python library to create hydrological models for rainfall-runoff prediction using deep learning methods. The repository includes implementations with Large-Sample Hydrology datasets such as CAMELS-GB, CAMELS-US, and CAMELS-DE. Besides data-driven architectures, the repository also supports hybrid hydrological models that combine machine learning with process-based knowledge.

The logic of the codes presented here is based on 'NeuralHydrology --- A Python library for Deep Learning research in hydrology' (https://github.com/neuralhydrology/neuralhydrology.git). For a more modular implementation of deep learning method in hydrological modeling we advice the use of Neural Hydrology.

Structure of the repository:

The codes presented in the repository are in the form of python scripts. Additionally several experiments are in the form of JupyterNotebooks for easy reproduction and execution. Following is a quick overview of the repository structure:

• benchmarks: Comparison of our library against other studies from scientific literature.
• data: Folder where the different datasets (e.g CAMELS-GB, CAMELS-US...) should be added. This information should be independently downloaded by the user.
• docs: Library documentation
• examples: Configuration files to run multiple examples.
• notebooks: Jupyter notebooks showing implementation examples, for different cases.
• results: Folder where the results generated by the codes will be stored.
• src/hy2dl: Code of the library.

Installation

A release version is available on PyPI and can be installed using:

uv

uv add hy2dl

or pip.

pip install hy2dl

The pyproject.toml file includes the package requirements.

Documentation:

Detailed documentation for the repository can be found at Hy2DL.readthedocs.io.

Citation:

If you find Hy²DL useful in your research or applications, please cite it as:

@software{hy2dl2025,
  author       = {Eduardo Acuña-Espinoza},
  title        = {Hy²DL: Hybrid Hydrological Modeling using Deep Learning methods},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.XXXXXXX},
  url          = {https://doi.org/10.5281/zenodo.XXXXXXX}
}