Articles | Volume 24, issue 12
Hydrol. Earth Syst. Sci., 24, 5759–5779, 2020
https://doi.org/10.5194/hess-24-5759-2020
Hydrol. Earth Syst. Sci., 24, 5759–5779, 2020
https://doi.org/10.5194/hess-24-5759-2020

Research article 03 Dec 2020

Research article | 03 Dec 2020

Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling

Ali Ghaseminejad and Venkatesh Uddameri

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Cited articles

Adamowski, J. and Chan, H. F.: A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 407, 28–40, https://doi.org/10.1016/j.jhydrol.2011.06.013, 2011. a
Adamowski, J., Adamowski, K., and Prokoph, A.: A spectral analysis based methodology to detect climatological influences on daily urban water demand, Math. Geosci., 45, 49–68, 2013. a
Adhikari, S., Belasco, E. J., and Knight, T. O.: Spatial producer heterogeneity in crop insurance product decisions within major corn producing states, Agric. Financ. Rev., 70, 66–78, 2010. a
Ahire, J.: Artificial Neural Networks: the Brain behind AI, Lulu. com, ISBN 1980483671, 9781980483670, 2018. a
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While artificial neural networks (ANNs) have been used to forecast groundwater levels at single wells, they have not been constructed to forecast hydraulic heads in both space and time. This seminal study presents a modeling framework, guided by the governing physical laws, for building an integrated space–time ANN (IST–ANN) model for regional groundwater level predictions. IST–ANN shows promise for parsimoniously modeling regional-scale groundwater levels using available surrogate information.