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Spatial predictions of soil properties are needed for various purposes.
The latter movements matched the analytical predictions of soil displacements caused by the groundwater oscillations.
The output images generated from the knowledge-based and classification tree models produced similar predictions of soil patterns across the landscape, with slightly different predictions of soil map units.
Model predictions of soil irradiance were excellent in orchards with symmetrical and elliptical canopies having a uniform leaf distribution.
Results also showed that predictions of soil taxa using 1 arc SRTM and 12 m WordDEM were identical.
Therefore, large-scale predictions of soil carbon fluxes in response to fire in the boreal region must explicitly incorporate topographic features.
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The predictions of soil-N models can be validated by comparison with observed data.
This comparison demonstrates the model's ability to provide accurate fit-free predictions of soil-temperature fields generated by GHEs.
Strong correlations exist for the UCS versus elastic modulus (R ⩾ 0.84) as well as for the UCS versus UPV (R ⩾ 0.82), which practically could be useful for the strength predictions of soil-cement columns.
Furthermore, an improved SCR soil interaction model, with accurate prediction of soil stiffness and riser penetration enables us to more accurately predict global riser dynamic performance in the TDZ.
Prediction of soil moisture is critical for water resources management.
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