Models of Landfill Leaching: Moisture Flow and Inorganic Strength

Straub, William A.; Lynch, Daniel R.

doi:10.1061/jeegav.0001276

Cited by 46 publications

(1 citation statement)

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“…Soil moisture, or volumetric water content (VWC), is an important property in a wide variety of fields including ecology ( D. A. Robinson et al, 2008 ), agriculture ( Blanchy et al, 2020 ), and environmental engineering ( Straub & Lynch, 1982 ). Direct measurement of soil moisture with probes is difficult because sensors must physically intrude into the soil, which locally disturbs natural conditions via compaction from probe insertion ( Iwata et al, 2017 ), or borehole installation.…”

Section: Introductionmentioning

confidence: 99%

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Terry

Day‐Lewis

Lane

et al. 2023

Vadose Zone Journal

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Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025-0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

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