Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-83-2023
https://doi.org/10.5194/hess-27-83-2023
Research article
 | 
02 Jan 2023
Research article |  | 02 Jan 2023

Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks

Xiaoying Zhang, Fan Dong, Guangquan Chen, and Zhenxue Dai

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Latest update: 19 Jul 2024
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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.