Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer...

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Bibliographic Details
Published in:Water (Basel) Vol. 13; no. 12; p. 1668
Main Authors: Moghaddam, Mohammad A., Ferre, Paul A. T., Ehsani, Mohammad Reza, Klakovich, Jeffrey, Gupta, Hoshin Vijay
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-06-2021
Subjects:
Algorithms
Boundary conditions
Conductivity
Deep learning
effective hydraulic conductivity
Energy dissipation
Hydraulics
Hydrogeology
Hydrologic systems
Hydrology
Information processing
Machine learning
Neural networks
Parameter estimation
Partial differential equations
Permeability
UNET
Weighting
Germany
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Summary:We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained directly on this information. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems.
ISSN:2073-4441
2073-4441
DOI:10.3390/w13121668