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 (K_eff ) of a binary K grid. A deep learning algorithm (UNET) can infer K_eff 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.