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: | Nov 12, 2021 at 4:48 p.m.
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| DOI: | 10.4211/hs.83ea5312635e44dc824eeb99eda12f06 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 2245 |
| Downloads: | 13688 |
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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
Related Resources
| This resource is referenced by | 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
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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