Articles | Volume 26, issue 22
https://doi.org/10.5194/hess-26-5793-2022
© Author(s) 2022. 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-26-5793-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Nov 2022
Research article |
| 17 Nov 2022
| 17 Nov 2022
How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models?
Reyhaneh Hashemi
CORRESPONDING AUTHOR
INRAE, Aix-Marseille University, RECOVER Research Unit, Aix-en-Provence, France
Pierre Brigode
Université Côte d'Azur, OCA, CNRS, IRD, GEOAZUR, France
INRAE, Paris-Saclay University, HYCAR Research Unit, Antony, France
Pierre-André Garambois
INRAE, Aix-Marseille University, RECOVER Research Unit, Aix-en-Provence, France
Pierre Javelle
INRAE, Aix-Marseille University, RECOVER Research Unit, Aix-en-Provence, France
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Cited
15 citations as recorded by crossref.
- Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM S. Yang et al. 10.1109/TGRS.2024.3384990
- Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network M. Al Mehedi et al. 10.1016/j.jhydrol.2023.130076
- Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
- Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production A. Sedai et al. 10.3390/forecast5010014
- Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
- 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
- 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
- Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI M. Park & H. Yang 10.3390/s24227205
- Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model J. Lee & J. Noh 10.3390/w15061036
- Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
- Pairing remote sensing and clustering in landscape hydrology for large-scale change identification: an application to the subarctic watershed of the George River (Nunavik, Canada) E. Sicaud et al. 10.5194/hess-28-65-2024
- Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare N. Alharbi et al. 10.3390/math11183942
- Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
- A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction M. Zhong et al. 10.1007/s11269-023-03583-0
15 citations as recorded by crossref.
- Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM S. Yang et al. 10.1109/TGRS.2024.3384990
- Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network M. Al Mehedi et al. 10.1016/j.jhydrol.2023.130076
- Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
- Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production A. Sedai et al. 10.3390/forecast5010014
- Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
- 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
- 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
- Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI M. Park & H. Yang 10.3390/s24227205
- Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model J. Lee & J. Noh 10.3390/w15061036
- Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
- Pairing remote sensing and clustering in landscape hydrology for large-scale change identification: an application to the subarctic watershed of the George River (Nunavik, Canada) E. Sicaud et al. 10.5194/hess-28-65-2024
- Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare N. Alharbi et al. 10.3390/math11183942
- Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
- A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction M. Zhong et al. 10.1007/s11269-023-03583-0
Latest update: 23 Nov 2024
Short summary
Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments....
