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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24 citations as recorded by crossref.
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- Reconstruction of long-term sea-level data gaps of tide gauge records using a neural network operator E. Lee et al. 10.3389/fmars.2022.1037697
- Use of long short-term memory network (LSTM) in the reconstruction of missing water level data in the River Seine I. Janbain et al. 10.1080/02626667.2023.2221791
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- A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting J. Khan et al. 10.3390/app13042743
- Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
- Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems J. Chen et al. 10.1029/2022WR032429
- Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model A. Anshuka et al. 10.1007/s00477-022-02204-3
- Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps L. Lopes Martins et al. 10.1080/02626667.2022.2145200
- Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series L. Kulanuwat et al. 10.3390/w13131862
- State Tagging for Improved Earth and Environmental Data Quality Assurance C. Tso et al. 10.3389/fenvs.2020.00046
- A Data Filling Methodology for Time Series Based on CNN and (Bi)LSTM Neural Networks K. Tzoumpas et al. 10.1109/ACCESS.2024.3369891
- GenerativeMap: Visualization and Exploration of Dynamic Density Maps via Generative Learning Model C. Chen et al. 10.1109/TVCG.2019.2934806
- Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 A. Derkacheva et al. 10.3390/rs12121935
- Gaussian clustering and jump-diffusion models of electricity prices: a deep learning analysis C. Mari & E. Mari 10.1007/s10203-021-00332-z
- Deep learning based regime-switching models of energy commodity prices C. Mari & E. Mari 10.1007/s12667-022-00515-6
- Occam’s razor, machine learning and stochastic modeling of complex systems: the case of the Italian energy market C. Mari & E. Mari 10.1007/s11135-023-01681-0
- Using Compressive Sampling to Fill Interbatch Data Gap From Low-Cost IoT Vibration Sensor B. Ooi et al. 10.1109/JIOT.2022.3151051
- Recent advancements of landslide hydrology R. Greco et al. 10.1002/wat2.1675
- Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
- Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River M. Sarafanov et al. 10.3390/w13243482
- Groundwater modelling of a shallow aquifer for the assessment of water logging: A case study of the Cebala‐Borj Touil irrigation perimeter, northern Tunisia H. Rzigui et al. 10.1002/ird.2723
9 citations as recorded by crossref.
- Daily Streamflow Time Series Modeling by Using a Periodic Autoregressive Model (ARMA) Based on Fuzzy Clustering M. Khazaeiathar et al. 10.3390/w14233932
- Improving Linear Interpolation of Missing Hydrological Data by Applying Integrated Autoregressive Models T. Niedzielski & M. Halicki 10.1007/s11269-023-03625-7
- Reconstruction of long-term sea-level data gaps of tide gauge records using a neural network operator E. Lee et al. 10.3389/fmars.2022.1037697
- Use of long short-term memory network (LSTM) in the reconstruction of missing water level data in the River Seine I. Janbain et al. 10.1080/02626667.2023.2221791
- Predicting future well performance for environmental remediation design using deep learning X. Song et al. 10.1016/j.jhydrol.2023.129110
- Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System D. Fronzi et al. 10.3390/w16010152
- A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting J. Khan et al. 10.3390/app13042743
- Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
- Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems J. Chen et al. 10.1029/2022WR032429
15 citations as recorded by crossref.
- Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model A. Anshuka et al. 10.1007/s00477-022-02204-3
- Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps L. Lopes Martins et al. 10.1080/02626667.2022.2145200
- Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series L. Kulanuwat et al. 10.3390/w13131862
- State Tagging for Improved Earth and Environmental Data Quality Assurance C. Tso et al. 10.3389/fenvs.2020.00046
- A Data Filling Methodology for Time Series Based on CNN and (Bi)LSTM Neural Networks K. Tzoumpas et al. 10.1109/ACCESS.2024.3369891
- GenerativeMap: Visualization and Exploration of Dynamic Density Maps via Generative Learning Model C. Chen et al. 10.1109/TVCG.2019.2934806
- Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 A. Derkacheva et al. 10.3390/rs12121935
- Gaussian clustering and jump-diffusion models of electricity prices: a deep learning analysis C. Mari & E. Mari 10.1007/s10203-021-00332-z
- Deep learning based regime-switching models of energy commodity prices C. Mari & E. Mari 10.1007/s12667-022-00515-6
- Occam’s razor, machine learning and stochastic modeling of complex systems: the case of the Italian energy market C. Mari & E. Mari 10.1007/s11135-023-01681-0
- Using Compressive Sampling to Fill Interbatch Data Gap From Low-Cost IoT Vibration Sensor B. Ooi et al. 10.1109/JIOT.2022.3151051
- Recent advancements of landslide hydrology R. Greco et al. 10.1002/wat2.1675
- Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
- Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River M. Sarafanov et al. 10.3390/w13243482
- Groundwater modelling of a shallow aquifer for the assessment of water logging: A case study of the Cebala‐Borj Touil irrigation perimeter, northern Tunisia H. Rzigui et al. 10.1002/ird.2723
Latest update: 18 Apr 2024
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...
