We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (K_eff) of a binary K grid. Machine learning and deep learning algorithms of varying complexity (decision tree, vanilla CNN, UNET) can infer K_eff with extremely high accuracy (R² > 0.99), even given only the fraction of the grid occupied by the high K medium. Adding information derived from the energy dissipation distribution improved each algorithm. However, all methods failed to infer K_eff accurately for outlier cases, all of which were inferred accurately using energy dissipation weighting directly. 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. 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.