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Pretrained models + simulations for our HESSD submission "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets"


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Resource type: Composite Resource
Storage: The size of this resource is 895.7 MB
Created: Jul 18, 2019 at 12:56 p.m.
Last updated: Dec 17, 2019 at 8:30 a.m.
DOI: 10.4211/hs.83ea5312635e44dc824eeb99eda12f06
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Sharing Status: Published
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Abstract

Contains all models trained for our publication "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets", as well as the evaluated model simulations. The set contains 48 runs in total, stemming from 3 different models (trained with 8 repetitions) and two different loss functions.

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Content

README.md

Pretrained models and model simulations

Contains all models trained for our publication "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets", as well as the evaluated model simulations. The set contains 48 runs in total, stemming from 3 different models (trained with 8 repetitions) and two different loss functions.

About

The models are part of our manuscript "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets" https://arxiv.org/abs/1907.08456 that is accepted for publication in HESS.

Code

The code for the paper can be found here https://github.com/kratzert/ealstm_regional_modeling

Contact

Frederik Kratzer: kratzert@ml.jku.at

References

Related Resources

The content of this resource is part of: Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 2019.

How to Cite

Kratzert, F. (2019). Pretrained models + simulations for our HESSD submission "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets", HydroShare, https://doi.org/10.4211/hs.83ea5312635e44dc824eeb99eda12f06

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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