Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-795-2022
© 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-26-795-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Feb 2022
Research article |
| 14 Feb 2022
| 14 Feb 2022
Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Valentina Radić
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Cited
19 citations as recorded by crossref.
- An illustration of model agnostic explainability methods applied to environmental data C. Wikle et al. 10.1002/env.2772
- Predicting Ili River streamflow change and identifying the major drivers with a novel hybrid model S. Liu et al. 10.1016/j.ejrh.2024.101807
- Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
- Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
- Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization A. Oliveira et al. 10.3390/w15050947
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Karst aquifer discharge response to rainfall interpreted as anomalous transport D. Elhanati et al. 10.5194/hess-28-4239-2024
- Modeling the Streamflow Response to Heatwaves Across Glacierized Basins in Southwestern Canada S. Anderson & V. Radić 10.1029/2023WR035428
- Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
- Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
- Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
- Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data Z. Hajibagheri et al. 10.1007/s11356-024-35482-1
- Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
- A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
- A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability S. Chidepudi et al. 10.1016/j.scitotenv.2022.161035
- 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
- In-stream <i>Escherichia coli</i> modeling using high-temporal-resolution data with deep learning and process-based models A. Abbas et al. 10.5194/hess-25-6185-2021
18 citations as recorded by crossref.
- An illustration of model agnostic explainability methods applied to environmental data C. Wikle et al. 10.1002/env.2772
- Predicting Ili River streamflow change and identifying the major drivers with a novel hybrid model S. Liu et al. 10.1016/j.ejrh.2024.101807
- Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
- Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
- Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization A. Oliveira et al. 10.3390/w15050947
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Karst aquifer discharge response to rainfall interpreted as anomalous transport D. Elhanati et al. 10.5194/hess-28-4239-2024
- Modeling the Streamflow Response to Heatwaves Across Glacierized Basins in Southwestern Canada S. Anderson & V. Radić 10.1029/2023WR035428
- Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
- Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
- Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
- Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data Z. Hajibagheri et al. 10.1007/s11356-024-35482-1
- Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
- A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
- A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability S. Chidepudi et al. 10.1016/j.scitotenv.2022.161035
- 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
Latest update: 22 Nov 2024
Short summary
We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction at more than 200 stream gauge stations in western Canada. We find the novel modelling style to work very well for daily streamflow prediction. Importantly, we interpret model learning to show that it has learned to focus on physically interpretable and physically relevant information, which is a highly desirable quality of machine-learning-based hydrological models.
We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction...
