Articles | Volume 26, issue 7
https://doi.org/10.5194/hess-26-1727-2022
© 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-26-1727-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
05 Apr 2022
Technical note |
| 05 Apr 2022
| 05 Apr 2022
Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks
Huiying Ren
Earth Systems Science Division, Pacific Northwest National Laboratory, Richland, WA, USA
Erol Cromwell
Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, WA, USA
Ben Kravitz
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
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40 citations as recorded by crossref.
- Reconstruction of long-term sea-level data gaps of tide gauge records using a neural network operator E. Lee et al.
- Incorporating artificial intelligence into the future of stormwater management M. Rahman et al.
- Microbial dormancy under freeze–thaw cycling regulates alpine soil responses to warming S. Qi et al.
- Daily soil temperature simulation at different depths in the Red River Basin: a long short-term memory approach M. Tahmasebi Nasab et al.
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- A GIS‐based tool for dynamic assessment of community susceptibility to flash flooding R. Wilkho et al.
- Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events Y. Yang et al.
- Evaluation of ICESat-2 Laser Altimetry for Inland Water Level Monitoring: A Case Study of Canadian Lakes Y. Kaya
- Towards a comprehensive decision support system for agroforestry systems A. Kheir et al.
- Predicting groundwater levels in coastal aquifers using deep learning models: a comparative study of sedimentary and metamorphic aquifers in nova scotia S. Samani et al.
- Application of swarm-based deep neural networks and ensemble models for reconstruction of specific conductance data A. Mahdavi-Meymand et al.
- An Adaptive Pattern Resonance System for Complex Adaptive Gap Filling of Environmental Datasets R. Gudla & N. Chang
- Evaluating Sentinel-2 gap filling techniques for cloud removal and data reconstruction S. Grich et al.
- A Bi-LSTM Attention Mechanism for Monitoring Seismic Events—Solving the Issue of Noise & Interpretability N. Iqbal et al.
- Use of long short-term memory network (LSTM) in the reconstruction of missing water level data in the River Seine I. Janbain et al.
- Exploring diurnal variation in soil moisture via sub-daily estimates reconstruction Z. Wei et al.
- Estimating missing daily streamflow data in a tropical basin with pronounced seasonal variability: A comparative case study from the Guayas River Basin, Ecuador D. Stay-Arevalo et al.
- A new method for predicting chlorophyll-a concentration in a reservoir: Coupling EFDC hydrodynamic and water quality model with ConvLSTM-MLP network H. Meng et al.
- Development of an Automatic Water Monitoring Network by Using Multi-Criteria Analysis and a GIS-Based Fuzzy Process S. Lagogiannis et al.
- Earth System Data Cubes: Avenues for advancing Earth system research D. Montero et al.
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- Power System Portfolio Selection and CO2 Emission Management Under Uncertainty Driven by a DNN-Based Stochastic Model C. Mari et al.
- Sub-daily global vegetation optical depth reconstruction from SMAP using 3D partial convolutional networks J. Kou et al.
- Integrating machine learning and physical models for rainfall-runoff prediction in the Upper Baro Akobo River Basin, Ethiopia Y. Belina et al.
- Kalman filtering assimilated machine learning methods significantly improve the prediction performance of water quality parameters Z. Gao et al.
- Deep learning based multi-temporal scale precipitation modeling for spring discharge prediction, Shentou springs, China C. Ma et al.
- Improving Linear Interpolation of Missing Hydrological Data by Applying Integrated Autoregressive Models T. Niedzielski & M. Halicki
- Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System D. Fronzi et al.
- Streamflow Simulation Using a Hybrid Approach Combining HEC-HMS and LSTM Model in the Tlawng River Basin of Mizoram, India S. Debbarma et al.
- A Hybrid BiLSTM-TE Architecture for Spring Discharge Prediction in Data-Scarce Regions Y. Liang et al.
- Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al.
- Daily Streamflow Time Series Modeling by Using a Periodic Autoregressive Model (ARMA) Based on Fuzzy Clustering M. Khazaeiathar et al.
- Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models E. Abdi et al.
- Filling gaps in PM2.5 time series: A broad evaluation from statistical to advanced neural network models R. Safarov et al.
- Predicting future well performance for environmental remediation design using deep learning X. Song et al.
- Reconstructing aquifer dynamics with machine learning: Linking irrigation expansion to groundwater decline in a data-scarce hyper-arid region S. Chucuya et al.
- Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems J. Chen et al.
- Correlation of environmental variables with Heteromastus filiformis habitat density: Gap-filling and machine learning approaches M. Yousefzadeh et al.
Saved (final revised paper)
Latest update: 09 May 2026
Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data...
