Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 14 April 2021 / Approved: 15 April 2021 / Online: 15 April 2021 (12:25:05 CEST)

How to cite: Abdolhosseini Moghaddam, M.; Ferré, T.P.A.; Klakovich, J.; Gupta, H.V.; Ehsani, M.R. Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?. Preprints 2021, 2021040412 (doi: 10.20944/preprints202104.0412.v1). Abdolhosseini Moghaddam, M.; Ferré, T.P.A.; Klakovich, J.; Gupta, H.V.; Ehsani, M.R. Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?. Preprints 2021, 2021040412 (doi: 10.20944/preprints202104.0412.v1).

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.

Subject Areas

deep learning; hydraulic conductivity; convolutional neural networks; groundwater

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