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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-117
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-117
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Apr 2020

03 Apr 2020

Review status
A revised version of this preprint was accepted for the journal HESS.

Physics-inspired integrated space-time Artificial Neural Networks for regional groundwater flow modeling

Ali Ghaseminejad and Venkatesh Uddameri Ali Ghaseminejad and Venkatesh Uddameri
  • Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409-1023

Abstract. An integrated space-time Artificial Neural Network (ANN) model inspired by the governing groundwater flow equation was developed to test whether a single ANN is capable of modeling regional groundwater flow systems. Model-independent entropy measures and random forest (RF) based feature selection procedures were used to identify suitable inputs for ANNs. L2 regularization, 5-fold cross-validation, and adaptive stochastic gradient descent (ADAM) algorithm led to a parsimonious ANN model for a 30 691 km2 agriculturally intensive area in the Ogallala Aquifer of Texas. The model testing at 38 independent wells during the 1956–2008 calibration period showed no overfitting issues and highlighted the model's ability to capture both the observed spatial dependence and temporal variability. The forecasting period (2009–2015) was marked by extreme climate variability in the region and served to evaluate the extrapolation capabilities of the model. While ANN models are universal interpolators, the model was able to capture the general trends and provided groundwater level estimates that were better than using historical means. Model sensitivity analysis indicated that pumping was the most sensitive process. Incorporation of spatial variability was more critical then capturing groundwater level persistence. The use of the standardized precipitation-evapotranspiration (SPEI) index as a surrogate for pumping was generally adequate but was unable to capture the heterogeneous groundwater extraction preferences of farmers under extreme climate conditions.

Ali Ghaseminejad and Venkatesh Uddameri

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Ali Ghaseminejad and Venkatesh Uddameri

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Data and Codes for A Physics-Inspired Integrated Space-Time Artificial Neural Network for Regional Groundwater Flow Modeling A. Ghaseminejad and V. Uddameri https://doi.org/10.17605/OSF.IO/5KV6R

Model code and software

Data and Codes for A Physics-Inspired Integrated Space-Time Artificial Neural Network for Regional Groundwater Flow Modeling A. Ghaseminejad and V. Uddameri https://doi.org/10.17605/OSF.IO/5KV6R

Ali Ghaseminejad and Venkatesh Uddameri

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Latest update: 28 Sep 2020
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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, to build an integrated space-time ANN (IST-ANN) model for regional groundwater level predictions. IST-ANN shows promise to parsimoniously model regional-scale groundwater levels using available surrogate information.
While Artificial Neural Networks (ANNs) have been used to forecast groundwater levels at single...
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