Python library to train neural networks with a strong focus on hydrological applications.
This package has been used extensively in research over the last years and was used in various academic publications. The core idea of this package is modularity in all places to allow easy integration of new datasets, new model architectures or any training-related aspects (e.g. loss functions, optimizer, regularization). One of the core concepts of this code base are configuration files, which let anyone train neural networks without touching the code itself. The NeuralHydrology package is built on top of the deep learning framework PyTorch, since it has proven to be the most flexible and useful for research purposes.
We (the AI for Earth Science group at the Institute for Machine Learning, Johannes Kepler University, Linz, Austria) are using this code in our day-to-day research and will continue to integrate our new research findings into this public repository.
- Documentation: neuralhydrology.readthedocs.io
- Research Blog: neuralhydrology.github.io
- Bug reports/Feature requests https://github.com/neuralhydrology/neuralhydrology/issues
In case you use NeuralHydrology in your research or work, it would be highly appreciated if you include a reference to our JOSS paper in any kind of publication.
@article{kratzert2022joss,
title = {NeuralHydrology --- A Python library for Deep Learning research in hydrology},
author = {Frederik Kratzert and Martin Gauch and Grey Nearing and Daniel Klotz},
journal = {Journal of Open Source Software},
publisher = {The Open Journal},
year = {2022},
volume = {7},
number = {71},
pages = {4050},
doi = {10.21105/joss.04050},
url = {https://doi.org/10.21105/joss.04050},
}For questions or comments regarding the usage of this repository, please use the discussion section on Github. For bug reports and feature requests, please open an issue on GitHub. In special cases, you can also reach out to us by email: neuralhydrology(at)googlegroups.com