Articles | Volume 28, issue 17
https://doi.org/10.5194/hess-28-4187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-28-4187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opinion article
| 
12 Sep 2024
Opinion article |
| 12 Sep 2024
| 12 Sep 2024
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Google Research, Vienna, Austria
Martin Gauch
Google Research, Zurich, Switzerland
Daniel Klotz
Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Grey Nearing
Google Research, Mountain View, California, USA
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Cited
16 citations as recorded by crossref.
- Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
- A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk A. Nayak et al. 10.1016/j.hydroa.2024.100189
- Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
- A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
- Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review I. Malashin et al. 10.3390/polym16233368
- Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features M. Gomez et al. 10.5194/hess-28-4407-2024
- A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
- Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
- To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
- Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
- Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
- Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
- Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
- Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble D. Spieler & N. Schütze 10.1029/2023WR036199
- A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
- Stream Nitrate Dynamics Driven Primarily by Discharge and Watershed Physical and Soil Characteristics at Intensively Monitored Sites: Insights From Deep Learning G. Gorski et al. 10.1029/2023WR036591
6 citations as recorded by crossref.
- Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
- A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk A. Nayak et al. 10.1016/j.hydroa.2024.100189
- Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
- A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
- Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review I. Malashin et al. 10.3390/polym16233368
- Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features M. Gomez et al. 10.5194/hess-28-4407-2024
10 citations as recorded by crossref.
- A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
- Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
- To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
- Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
- Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
- Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
- Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
- Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble D. Spieler & N. Schütze 10.1029/2023WR036199
- A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
- Stream Nitrate Dynamics Driven Primarily by Discharge and Watershed Physical and Soil Characteristics at Intensively Monitored Sites: Insights From Deep Learning G. Gorski et al. 10.1029/2023WR036591
Latest update: 13 Dec 2024
Short summary
Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Recently, a special type of neural-network architecture became increasingly popular in hydrology...
Special issue