Articles | Volume 24, issue 12
https://doi.org/10.5194/hess-24-5759-2020
© Author(s) 2020. 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-24-5759-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Dec 2020
Research article |
| 03 Dec 2020
| 03 Dec 2020
Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling
Ali Ghaseminejad
Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX, 79409, USA
Venkatesh Uddameri
CORRESPONDING AUTHOR
Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX, 79409, USA
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13 citations as recorded by crossref.
- Elucidating the relationship between gaseous O2 and redox potential in a soil aquifer treatment system using data driven approaches and an oxygen diffusion model T. Turkeltaub et al. 10.1016/j.jhydrol.2023.129168
- Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting M. Asif et al. 10.1007/s11269-025-04093-x
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- Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways N. Zeydalinejad & R. Dehghani 10.1016/j.gsd.2022.100882
- A Systematic Literature Review of MODFLOW Combined with Artificial Neural Networks (ANNs) for Groundwater Flow Modelling K. Kishor et al. 10.3390/w17162375
- Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models D. Roy et al. 10.3390/w13213130
- Integral delay inspired deep learning model for single pool water level prediction X. Lei et al. 10.1016/j.jhydrol.2025.133328
- Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques Y. Zhou et al. 10.1016/j.jhydrol.2021.127255
- Modelling clogging dynamics in groundwater systems using multiscale homogenized physics informed neural network (MHPINN) A. Chew et al. 10.1016/j.jestch.2023.101605
- A deep learning approach for energy management systems in smart buildings towards a low-carbon economy D. Gao 10.1093/ijlct/ctaf063
- Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions S. Nozari et al. 10.1016/j.jhydrol.2022.128511
- Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review A. Chew et al. 10.1007/s11831-024-10145-z
3 citations as recorded by crossref.
- Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling D. Roy et al. 10.1007/s11269-021-02787-6
- Estimation of actual evapotranspiration and water requirements of strategic crops under different stresses H. Ramezani Etedali et al. 10.1038/s41598-025-92481-z
- Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling A. Ghaseminejad & V. Uddameri 10.5194/hess-24-5759-2020
Latest update: 17 Sep 2025
Short summary
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.
While artificial neural networks (ANNs) have been used to forecast groundwater levels at single...
