neuralhydrology-basinnetworks

Python library to train neural networks with a strong focus on hydrological applications.

https://github.com/quinnylee/neuralhydrology-basinnetworks

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README
  • Academic publication links
    Links to: joss.theoj.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (15.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Python library to train neural networks with a strong focus on hydrological applications.

Basic Info
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  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 11 months ago · Last pushed 9 months ago
Metadata Files
Readme Contributing License Citation Codeowners

README.md

#

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.

Cite NeuralHydrology

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.

bibtex @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}, }

Contact

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

Owner

  • Name: Quinn Lee
  • Login: quinnylee
  • Kind: user

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Kratzert"
  given-names: "Frederik"
- family-names: "Gauch"
  given-names: "Martin"
- family-names: "Nearing"
  given-names: "Grey"
- family-names: "Klotz"
  given-names: "Daniel"
title: "NeuralHydrology --- A Python library for Deep Learning research in hydrology"
doi: 10.21105/joss.04050
url: "https://doi.org/10.21105/joss.04050"
preferred-citation:
  type: article
  authors:
  - family-names: "Kratzert"
    given-names: "Frederik"
  - family-names: "Gauch"
    given-names: "Martin"
  - family-names: "Nearing"
    given-names: "Grey"
  - family-names: "Klotz"
    given-names: "Daniel"
  doi: "10.21105/joss.04050"
  journal: "Journal of Open Source Software"
  publisher: 
    name: "The Open Journal"
  start: 4050 # First page number
  end: 4050 # Last page number
  title: "NeuralHydrology --- A Python library for Deep Learning research in hydrology"
  number: 71
  volume: 7
  year: 2022

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Dependencies

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