Effective hydraulic conductivity and diffusivity of randomly heterogeneous porous solids with compressible constituents

ABSTRACT The objective of this study was to determine the effective hydraulic conductivity of six areas located in the Cerrado region of Mato Grosso, Brazil, and to identify physical attributes of soils with potential for predicting effective hydraulic conductivity. The tests to determine the effective hydraulic conductivity were carried out in six areas, covering the textural classes sand, sandy loam and clay, and the following uses: pasture, Cerrado and agriculture. Particle size, sand fractionation, total carbon content, degree of clay flocculation, bulk density, macroporosity, microporosity, mean weight diameter, mean geometric diameter and aggregate stability index were determined. From the data, statistical analyses of contrasts were performed by the Kruskal - Wallis test, and simple Pearson’s correlation coefficient was determined between variables. The average values of effective hydraulic conductivity for the pasture, agriculture and Cerrado areas were 95.73, 27.83 and 48.31 mm h-1, respectively. Higher value of effective hydraulic conductivity was observed in the Pasture area point 2 when compared to the Agriculture area point 2, because the amount of clay determined in Agriculture area was approximately 16 times greater than that of the area Pasture point 2, conditioning lower water infiltration in the soil profile of the area Agriculture point 2. Among the physical attributes analyzed, those with the highest potential for Ke prediction were: clay, silt, sand (coarse, medium and fine), total carbon and aggregate stability index.

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

10.20944/preprints202104.0412.v1 ◽

2021 ◽

Author(s):

Mohammad Abdolhosseini Moghaddam ◽

Ty Paul Andrew Ferré ◽

Jeffrey Klakovich ◽

Hoshin Vijay Gupta ◽

Mohammad Reza Ehsani

Keyword(s):

Deep Learning ◽

Energy Dissipation ◽

Hydraulic Conductivity ◽

Learning Algorithm ◽

High Fidelity ◽

Effective Hydraulic Conductivity ◽

Deep Learning Algorithm ◽

Localized Structures ◽

Hydrologic Systems ◽

Controlled Flow

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.

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Effective Hydraulic Conductivity of Three‐Dimensional Heterogeneous Formations of Lognormal Permeability Distribution: The Impact of Connectivity

Water Resources Research ◽

10.1002/2017wr022141 ◽

2018 ◽

Vol 54 (3) ◽

pp. 2480-2486 ◽

Cited By ~ 5

Author(s):

A. Zarlenga ◽

I. Janković ◽

A. Fiori ◽

G. Dagan

Keyword(s):

Hydraulic Conductivity ◽

Three Dimensional ◽

Effective Hydraulic Conductivity ◽

Permeability Distribution ◽

Heterogeneous Formations ◽

The Impact

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