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Pretrained models + simulations for our HESSD submission "Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling"


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Created: Jul 18, 2019 at 12:56 p.m.
Last updated: Jul 22, 2019 at 8:51 a.m.
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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

Kratzert, F. (2019). Pretrained models + simulations for our HESSD submission "Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling", HydroShare, http://www.hydroshare.org/resource/83ea5312635e44dc824eeb99eda12f06

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

 http://creativecommons.org/licenses/by/4.0/
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