Pretrained models + simulations for our HESSD submission "Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling"
| Authors: | Frederik Kratzert |
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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: | Jul 22, 2019 at 8:51 a.m.
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| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Downloads: | 2 |
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Abstract
Contains all models trained for our publication "Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling", 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 "Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling", 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 "Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling" https://arxiv.org/abs/1907.08456 that is submited to HESSD.
Code
The code for the paper can be found here https://github.com/kratzert/ealstm_regional_modeling
Contact
Frederik Kratzer: kratzert@ml.jku.at
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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