Articles | Volume 27, issue 1
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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Cited articles

Abdalla, O. A. and Al-Rawahi, A. S.: Groundwater recharge dams in arid areas as tools for aquifer replenishment and mitigating seawater intrusion: example of AlKhod, Oman, Environ. Earth Sci., 69, 1951–1962, 2013. 
Afaq, S. and Rao, S.: Significance of epochs on training a neural network, Int. J. Scient. Technol. Res., 9, 485–488, 2020. 
Baena-Ruiz, L., Pulido-Velazquez, D., Collados-Lara, A.-J., Renau-Pruñonosa, A., and Morell, I.: Global assessment of seawater intrusion problems (status and vulnerability), Water Resour. Manage., 32, 2681–2700, 2018. 
Bai, S., Kolter, J. Z., and Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv:1803.01271,, 2018. 
Barlow, P. M. and Reichard, E. G.: Saltwater intrusion in coastal regions of North America, Hydrogeol. J., 18, 247–260, 2010. 
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.