Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5493-2022
© 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-26-5493-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
04 Nov 2022
Technical note |
| 04 Nov 2022
| 04 Nov 2022
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Grey S. Nearing
CORRESPONDING AUTHOR
Google Research, Mountain View, CA, USA
Daniel Klotz
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Jonathan M. Frame
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Martin Gauch
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Oren Gilon
Google Research, Tel Aviv, Israel
Frederik Kratzert
Google Research, Vienna, Austria
Alden Keefe Sampson
Upstream Tech, Alameda, CA, USA
Guy Shalev
Google Research, Tel Aviv, Israel
Sella Nevo
Google Research, Tel Aviv, Israel
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Cited
21 citations as recorded by crossref.
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
- Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al. 10.3390/w15223944
- Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method S. Yang et al. 10.1007/s11269-023-03725-4
- Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. 10.1029/2023WR035676
- How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
- 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
- Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
- Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
- HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
- Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting C. Liu et al. 10.1016/j.jenvman.2024.121466
- Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
- Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies N. Wang et al. 10.1029/2022WR033644
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
- Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations J. Zwart et al. 10.3389/frwa.2023.1184992
- rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
- Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions J. Zwart et al. 10.1111/1752-1688.13093
- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al. 10.5194/hess-26-5793-2022
15 citations as recorded by crossref.
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
- Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al. 10.3390/w15223944
- Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method S. Yang et al. 10.1007/s11269-023-03725-4
- Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. 10.1029/2023WR035676
- How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
- 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
- Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
- Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
- HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
- Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting C. Liu et al. 10.1016/j.jenvman.2024.121466
- Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
- Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies N. Wang et al. 10.1029/2022WR033644
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
6 citations as recorded by crossref.
- The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
- Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations J. Zwart et al. 10.3389/frwa.2023.1184992
- rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
- Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions J. Zwart et al. 10.1111/1752-1688.13093
- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al. 10.5194/hess-26-5793-2022
Latest update: 20 Nov 2024
Short summary
When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on
bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.
When designing flood forecasting models, it is necessary to use all available data to achieve...
