Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-83-2023
© Author(s) 2023. 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-27-83-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2023
Research article |
| 02 Jan 2023
| 02 Jan 2023
Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks
Xiaoying Zhang
Institute of Intelligent Simulation and Early Warning for
Subsurface Environment, Jilin University, Changchun, China
College of Construction Engineering, Jilin University, Changchun,
China
Fan Dong
Institute of Intelligent Simulation and Early Warning for
Subsurface Environment, Jilin University, Changchun, China
College of Construction Engineering, Jilin University, Changchun,
China
Guangquan Chen
Key Laboratory of Marine Sedimentology and Environmental Geology,
First Institute of Oceanography, State Oceanic Administration, Qingdao, China
Zhenxue Dai
CORRESPONDING AUTHOR
Institute of Intelligent Simulation and Early Warning for
Subsurface Environment, Jilin University, Changchun, China
College of Construction Engineering, Jilin University, Changchun,
China
Viewed
Total article views: 3,789 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Jul 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,575 | 1,103 | 111 | 3,789 | 104 | 145 |
- HTML: 2,575
- PDF: 1,103
- XML: 111
- Total: 3,789
- BibTeX: 104
- EndNote: 145
Total article views: 3,105 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Jan 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,169 | 841 | 95 | 3,105 | 90 | 128 |
- HTML: 2,169
- PDF: 841
- XML: 95
- Total: 3,105
- BibTeX: 90
- EndNote: 128
Total article views: 684 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Jul 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 406 | 262 | 16 | 684 | 14 | 17 |
- HTML: 406
- PDF: 262
- XML: 16
- Total: 684
- BibTeX: 14
- EndNote: 17
Viewed (geographical distribution)
Total article views: 3,789 (including HTML, PDF, and XML)
Thereof 3,685 with geography defined
and 104 with unknown origin.
Total article views: 3,105 (including HTML, PDF, and XML)
Thereof 3,037 with geography defined
and 68 with unknown origin.
Total article views: 684 (including HTML, PDF, and XML)
Thereof 648 with geography defined
and 36 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
31 citations as recorded by crossref.
- Spatiotemporal prediction for groundwater heavy metal contamination using Soft-DTW-based clustering and graph neural network framework Y. He et al.
- Interpretable Deep Learning for Characterizing Sinkhole to Supply Well Transfer Dynamics in Karst Aquifers B. Nigon et al.
- Experimental review and recent advances in deep learning techniques for solar irradiance forecasting and prediction C. Otuka et al.
- A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition F. Mortezapour Shiri et al.
- Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation C. Ni et al.
- Persistent Oscillations in the Decadal Prediction of Central Mediterranean Wetting and Drying Phases N. Diodato et al.
- Interpretable deep learning for sewer network water level forecasting in a Northern Chinese City: Implications for enhancing real-time assessment of system operational conditions Z. Yi et al.
- Wavelet gated multiformer for groundwater time series forecasting V. Serravalle Reis Rodrigues 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.
- Physics-informed neural networks for modelling groundwater flow and solute transport in stressed coastal aquifers S. Al Jabri et al.
- Interval Prediction Model for Seepage Flow in Earth–Rock Dams Based on Time Series Characteristics S. Yang et al.
- Prediction of groundwater level under the influence of groundwater exploitation using a data-driven method with the combination of time series analysis and long short-term memory: a case study of a coastal aquifer in Rizhao City, Northern China B. Guo et al.
- Study on the Variation in Coastal Groundwater Levels under High-Intensity Brine Extraction Conditions Q. Su et al.
- Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index P. Singh et al.
- A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction S. Huan
- An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications A. Ali et al.
- Investigation into groundwater level prediction within a deep learning framework: Incorporating the spatial dynamics of adjacent wells Z. Han et al.
- Assessing atmospheric influences for improving time-varying data-driven decadal predictions of Mediterranean spring discharge N. Diodato et al.
- Forecasting groundwater levels using machine learning methods: The case of California’s Central Valley G. May-Lagunes et al.
- Multi-head attention driven aggregation-differentiation network for probabilistic groundwater depth forecasting and depth-stratified early warning: A multi-site efficient framework J. Xu et al.
- Groundwater level forecasting with machine learning models: A review K. Boo et al.
- Development of data-driven statistical models for daily water table depth prediction and variable selection: Two case studies in coastal plain forested wetlands A. Manna et al.
- Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa N. Igwebuike et al.
- Groundwater depth prediction based on CNN-GRU-attention model H. Wei et al.
- Analysis and prediction of groundwater salinity on Yongxing Island using multiple machine learning models integrated with sparrow search algorithm C. Xia et al.
- Artificial intelligence-driven forecast of methane recovery and CO2 storage efficiency for carbon-neutral energy production from marine gas hydrates H. Vo Thanh et al.
- Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements V. Reddy M et al.
- A Comprehensive Overview and Comparative Analysis on Deep Learning Models T. Perumal et al.
- Groundwater Level Prediction Using a Hybrid TCN–Transformer–LSTM Model and Multi-Source Data Fusion: A Case Study of the Kuitun River Basin, Xinjiang Y. Liu et al.
- Application of Hybrid Machine Learning for Groundwater Level Prediction: A Comprehensive Review C. Biswakalyani et al.
- A systematic review of machine learning models for groundwater level prediction G. Jesse et al.
31 citations as recorded by crossref.
- Spatiotemporal prediction for groundwater heavy metal contamination using Soft-DTW-based clustering and graph neural network framework Y. He et al.
- Interpretable Deep Learning for Characterizing Sinkhole to Supply Well Transfer Dynamics in Karst Aquifers B. Nigon et al.
- Experimental review and recent advances in deep learning techniques for solar irradiance forecasting and prediction C. Otuka et al.
- A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition F. Mortezapour Shiri et al.
- Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation C. Ni et al.
- Persistent Oscillations in the Decadal Prediction of Central Mediterranean Wetting and Drying Phases N. Diodato et al.
- Interpretable deep learning for sewer network water level forecasting in a Northern Chinese City: Implications for enhancing real-time assessment of system operational conditions Z. Yi et al.
- Wavelet gated multiformer for groundwater time series forecasting V. Serravalle Reis Rodrigues 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.
- Physics-informed neural networks for modelling groundwater flow and solute transport in stressed coastal aquifers S. Al Jabri et al.
- Interval Prediction Model for Seepage Flow in Earth–Rock Dams Based on Time Series Characteristics S. Yang et al.
- Prediction of groundwater level under the influence of groundwater exploitation using a data-driven method with the combination of time series analysis and long short-term memory: a case study of a coastal aquifer in Rizhao City, Northern China B. Guo et al.
- Study on the Variation in Coastal Groundwater Levels under High-Intensity Brine Extraction Conditions Q. Su et al.
- Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index P. Singh et al.
- A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction S. Huan
- An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications A. Ali et al.
- Investigation into groundwater level prediction within a deep learning framework: Incorporating the spatial dynamics of adjacent wells Z. Han et al.
- Assessing atmospheric influences for improving time-varying data-driven decadal predictions of Mediterranean spring discharge N. Diodato et al.
- Forecasting groundwater levels using machine learning methods: The case of California’s Central Valley G. May-Lagunes et al.
- Multi-head attention driven aggregation-differentiation network for probabilistic groundwater depth forecasting and depth-stratified early warning: A multi-site efficient framework J. Xu et al.
- Groundwater level forecasting with machine learning models: A review K. Boo et al.
- Development of data-driven statistical models for daily water table depth prediction and variable selection: Two case studies in coastal plain forested wetlands A. Manna et al.
- Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa N. Igwebuike et al.
- Groundwater depth prediction based on CNN-GRU-attention model H. Wei et al.
- Analysis and prediction of groundwater salinity on Yongxing Island using multiple machine learning models integrated with sparrow search algorithm C. Xia et al.
- Artificial intelligence-driven forecast of methane recovery and CO2 storage efficiency for carbon-neutral energy production from marine gas hydrates H. Vo Thanh et al.
- Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements V. Reddy M et al.
- A Comprehensive Overview and Comparative Analysis on Deep Learning Models T. Perumal et al.
- Groundwater Level Prediction Using a Hybrid TCN–Transformer–LSTM Model and Multi-Source Data Fusion: A Case Study of the Kuitun River Basin, Xinjiang Y. Liu et al.
- Application of Hybrid Machine Learning for Groundwater Level Prediction: A Comprehensive Review C. Biswakalyani et al.
- A systematic review of machine learning models for groundwater level prediction G. Jesse et al.
Saved (final revised paper)
Latest update: 02 May 2026
Short summary
In a data-driven framework, groundwater levels can generally only be calculated 1 time step ahead. We discuss the advance prediction with longer forecast periods rather than single time steps by constructing a model based on a temporal convolutional network. Model accuracy and efficiency were further compared with an LSTM-based model. The two models derived in this study can help people cope with the uncertainty of what might occur in hydrological scenarios under the threat of climate change.
In a data-driven framework, groundwater levels can generally only be calculated 1 time step...
