Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-945-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-945-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Feb 2024
Research article |
| 27 Feb 2024
| 27 Feb 2024
Toward interpretable LSTM-based modeling of hydrological systems
Luis Andres De la Fuente
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson 85721, United States
Mohammad Reza Ehsani
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson 85721, United States
Hoshin Vijai Gupta
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson 85721, United States
Laura Elizabeth Condon
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson 85721, United States
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Cited
14 citations as recorded by crossref.
- Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study K. Islam et al. 10.1371/journal.pwat.0000359
- Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia N. Andika et al. 10.1016/j.rines.2025.100090
- Streamflow Forecasting: A Comparative Analysis of ARIMAX, Rolling Forecasting LSTM Neural Network and Physically Based Models in a Pristine Catchment D. Perazzolo et al. 10.3390/w17152341
- Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al. 10.3390/w17030339
- Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. 10.1016/j.envsoft.2025.106648
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al. 10.1016/j.jhydrol.2025.134082
- How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al. 10.5194/hess-29-5005-2025
- Integrated hydrological modelling and streamflow characterization of Gangotri Glacier meltwater M. Arora et al. 10.1007/s13201-024-02283-3
- Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. 10.1016/j.jhydrol.2025.134270
- Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv 10.1016/j.jhydrol.2025.133033
- An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM F. Hu et al. 10.1016/j.envsoft.2025.106524
- Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Using Deep Learning in Ensemble Streamflow Forecasting: Exploring the Predictive Value of Explicit Snowpack Information P. Modi et al. 10.1029/2024MS004582
12 citations as recorded by crossref.
- Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study K. Islam et al. 10.1371/journal.pwat.0000359
- Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia N. Andika et al. 10.1016/j.rines.2025.100090
- Streamflow Forecasting: A Comparative Analysis of ARIMAX, Rolling Forecasting LSTM Neural Network and Physically Based Models in a Pristine Catchment D. Perazzolo et al. 10.3390/w17152341
- Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al. 10.3390/w17030339
- Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. 10.1016/j.envsoft.2025.106648
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al. 10.1016/j.jhydrol.2025.134082
- How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al. 10.5194/hess-29-5005-2025
- Integrated hydrological modelling and streamflow characterization of Gangotri Glacier meltwater M. Arora et al. 10.1007/s13201-024-02283-3
- Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. 10.1016/j.jhydrol.2025.134270
- Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv 10.1016/j.jhydrol.2025.133033
- An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM F. Hu et al. 10.1016/j.envsoft.2025.106524
- Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
2 citations as recorded by crossref.
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Using Deep Learning in Ensemble Streamflow Forecasting: Exploring the Predictive Value of Explicit Snowpack Information P. Modi et al. 10.1029/2024MS004582
Latest update: 18 Oct 2025
Short summary
Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is...
