Hybrid3D (integrated modeling): a 3D theory-guided machine learning algorithm for inverse modeling of variably saturated groundwater flow coupled with surface flow dynamics

Published: 22 December 2022| Version 1 | DOI: 10.17632/g2f4g4j5kr.1
Adoubi Vincent De Paul ADOMBI,


Hybrid3D is a new fully modifiable code for inverse modeling of variably saturated groundwater flow coupled with surface flow dynamics. Summary of what is new: 1) The idea of considering each component of the overall water system as a theory-guided model that exchanges information with other models that are themselves theory-guided. 2) First integrated theory-guided machine learning code for spatio-temporal groundwater level simulation. 3) First code for the simulation of aquifers with variable saturation. Details: The code is initially built for a real aquifer with an irregular geometry composed of 4 stratigraphic units, each one formed by different hydrogeological layers. In this aquifer, we have 4 hydrogeological layers: 3 layers of sands with variable granulometry and one layer of clay. The conceptual model of this aquifer can be found in Adombi et al (2022)*. Since this is an integrated model, the new idea was to consider each component of the system (aquifer + surface water) as a model that exchanges information with the other model. For example, the aquifer was considered as a variably saturated medium and is represented by a theory-guided artificial neural network ("variably saturated flow equations") and the surface water by another theory-guided artificial neural network ("2D Saint Venant equations"). The code can also be modified to consider the pumping wells as a third model (theory-guided neural network) that interacts with the other two. The retention and permeability laws used are those of Brooks & Corey (1964) but can be modified to use any retention and permeability laws. In this code, the Saint Venant equations are incompletely constrained. The user has the choice, if data are available, to increase the constraint on these equations for the surface water system. All code details are provided in the python files. A folder containing the data is also added. So the code can be executed directly after the download. Requirements: TensorFlow version: 2.3 or less References: Adombi, A.V.D.P., Chesnaux, R., Boucher, M.-A., 2022. Comparing numerical modelling, traditional machine learning and theory-guided machine learning in inverse modeling of groundwater dynamics: A first study case application. Journal of Hydrology: 128600. DOI:https://doi.org/10.1016/j.jhydrol.2022.128600. Brooks, R.H. and Corey, A.T., 1964. Hydraulic Properties of Porous Media. Hydrology Paper: Vol. 3, Colorado State University, Fort Collins.



Universite du Quebec a Chicoutimi


Groundwater, Machine Learning