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

Viewed

Total article views: 1,864 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,322 489 53 1,864 34 39
  • HTML: 1,322
  • PDF: 489
  • XML: 53
  • Total: 1,864
  • BibTeX: 34
  • EndNote: 39
Views and downloads (calculated since 22 Jul 2022)
Cumulative views and downloads (calculated since 22 Jul 2022)

Viewed (geographical distribution)

Total article views: 1,864 (including HTML, PDF, and XML) Thereof 1,763 with geography defined and 101 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jul 2024
Download
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.