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
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Cited
17 citations as recorded by crossref.
- Study on the Variation in Coastal Groundwater Levels under High-Intensity Brine Extraction Conditions Q. Su et al. 10.3390/su152316199
- Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index P. Singh et al. 10.1109/ACCESS.2025.3568634
- A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction S. Huan 10.1016/j.jhydrol.2023.130034
- An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications A. Ali et al. 10.1155/2024/9480522
- Investigation into groundwater level prediction within a deep learning framework: Incorporating the spatial dynamics of adjacent wells Z. Han et al. 10.1016/j.jhydrol.2025.133097
- Assessing atmospheric influences for improving time-varying data-driven decadal predictions of Mediterranean spring discharge N. Diodato et al. 10.1080/02626667.2025.2480128
- Forecasting groundwater levels using machine learning methods: The case of California’s Central Valley G. May-Lagunes et al. 10.1016/j.hydroa.2023.100161
- A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition F. Mortezapour Shiri et al. 10.3390/app15062984
- Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation C. Ni et al. 10.3934/math.2024714
- Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
- Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa N. Igwebuike et al. 10.1007/s12145-024-01623-w
- Persistent Oscillations in the Decadal Prediction of Central Mediterranean Wetting and Drying Phases N. Diodato et al. 10.34133/olar.0093
- Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements V. Reddy M et al. 10.1109/LSENS.2024.3453558
- A Comprehensive Overview and Comparative Analysis on Deep Learning Models T. Perumal et al. 10.32604/jai.2024.054314
- Wavelet gated multiformer for groundwater time series forecasting V. Serravalle Reis Rodrigues et al. 10.1038/s41598-023-39688-0
- 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. 10.1007/s12145-025-01856-3
- 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. 10.3389/fenvs.2023.1253949
17 citations as recorded by crossref.
- Study on the Variation in Coastal Groundwater Levels under High-Intensity Brine Extraction Conditions Q. Su et al. 10.3390/su152316199
- Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index P. Singh et al. 10.1109/ACCESS.2025.3568634
- A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction S. Huan 10.1016/j.jhydrol.2023.130034
- An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications A. Ali et al. 10.1155/2024/9480522
- Investigation into groundwater level prediction within a deep learning framework: Incorporating the spatial dynamics of adjacent wells Z. Han et al. 10.1016/j.jhydrol.2025.133097
- Assessing atmospheric influences for improving time-varying data-driven decadal predictions of Mediterranean spring discharge N. Diodato et al. 10.1080/02626667.2025.2480128
- Forecasting groundwater levels using machine learning methods: The case of California’s Central Valley G. May-Lagunes et al. 10.1016/j.hydroa.2023.100161
- A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition F. Mortezapour Shiri et al. 10.3390/app15062984
- Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation C. Ni et al. 10.3934/math.2024714
- Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
- Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa N. Igwebuike et al. 10.1007/s12145-024-01623-w
- Persistent Oscillations in the Decadal Prediction of Central Mediterranean Wetting and Drying Phases N. Diodato et al. 10.34133/olar.0093
- Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements V. Reddy M et al. 10.1109/LSENS.2024.3453558
- A Comprehensive Overview and Comparative Analysis on Deep Learning Models T. Perumal et al. 10.32604/jai.2024.054314
- Wavelet gated multiformer for groundwater time series forecasting V. Serravalle Reis Rodrigues et al. 10.1038/s41598-023-39688-0
- 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. 10.1007/s12145-025-01856-3
- 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. 10.3389/fenvs.2023.1253949
Latest update: 17 Jun 2025
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...
