Skip to main content

Machine learning for CAMELS_GB (support for more in the future maybe)

Project description

# Main code for my master thesis

# Installation - If you use pipenv and pyenv: ` pipenv install -e git+https://github.com/bernharl/ealstm_regional_modeling_camels_gb.git#egg=camelsml --python 3.8 ` - If not using pipenv, this repository should be installable using pip as well.

## Content of the repository This repo is structured like a Python package. All relevant code is found within the camelsml directory.

## Citation

As you can see on the Github page, this repository is a fork of [this repository](https://github.com/kratzert/ealstm_regional_modeling). Therefore, if you use this code, make sure to cite:

` @article{kratzert2019universal, author = {Kratzert, F. and Klotz, D. and Shalev, G. and Klambauer, G. and Hochreiter, S. and Nearing, G.}, title = {Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets}, journal = {Hydrology and Earth System Sciences}, volume = {23}, year = {2019}, number = {12}, pages = {5089--5110}, url = {https://www.hydrol-earth-syst-sci.net/23/5089/2019/}, doi = {10.5194/hess-23-5089-2019} } ` , as well as the thesis connected to this code.

## License [Apache License 2.0](https://github.com/kratzert/ealstm_regional_modeling/blob/master/LICENSE)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

camelsml-2.0.1.tar.gz (34.7 kB view details)

Uploaded Source

File details

Details for the file camelsml-2.0.1.tar.gz.

File metadata

  • Download URL: camelsml-2.0.1.tar.gz
  • Upload date:
  • Size: 34.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.6

File hashes

Hashes for camelsml-2.0.1.tar.gz
Algorithm Hash digest
SHA256 9e3c514a8497093cb1486d1a2a333bae42abdb6b4a8fb457297755938fc7d91c
MD5 487fcace88904ad0ee8941ced771ff0e
BLAKE2b-256 49192e6cae07931394560016416655e99c76fb058d3d2fac8f715808bdf1df27

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page