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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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...
