Moghaddam, M.A.; Ferre, P.A.A.T.; Ehsani, M.R.; Klakovich, J.; Gupta, H.V. Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields? Water2021, 13, 1668.
Moghaddam, M.A.; Ferre, P.A.A.T.; Ehsani, M.R.; Klakovich, J.; Gupta, H.V. Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields? Water 2021, 13, 1668.
Moghaddam, M.A.; Ferre, P.A.A.T.; Ehsani, M.R.; Klakovich, J.; Gupta, H.V. Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields? Water2021, 13, 1668.
Moghaddam, M.A.; Ferre, P.A.A.T.; Ehsani, M.R.; Klakovich, J.; Gupta, H.V. Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields? Water 2021, 13, 1668.
Abstract
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 with high fidelity, 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 on this information directly. 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 most effectively.
Keywords
deep learning; hydraulic conductivity; convolutional neural networks; groundwater
Subject
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
