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We planned a two-phase soil survey based on regression kriging (RK).
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The objective of this work was to compare three spatially explicit modeling approaches for soil depth (SD) for France (540 K km2), produced using: i) a straight forward digital soil mapping (DSM) approach, based on regression treemodeling (RTM), ii) gradient boosting modeling (GBM), and iii) multi-resolution kriging (MrK) for large datasets.
Predictions from block kriging are based on kriging using a regular grid and then averaging the values within each block.
In this work, a novel method for surrogate-based feasibility analysis based on kriging metamodels will be presented.
The utilized approach, based on kriging with external drift (or its extension, i.e. regression kriging), permits the researcher to take into account auxiliary information, evaluate the related prediction uncertainty, and highlight abrupt variations in subsoil resonance frequencies.
We propose a surrogate based optimisation procedure based on kriging for the design of PSA systems.
Based on these conclusions, Kriging modeling is, secondly, proposed to robustly predict friction induced instabilities.
The analysis is based on kriging response-surfaces built during the minimization of the objective function.
In this paper we use this information to develop a spatio-temporal interpolation method and demonstration based on kriging.
Specifically, it utilizes a newly developed iterative sampling method based on Kriging fitting to efficiently determine optimal control actions.
Spatial heterogeneity in the reservoir was evaluated by means of sampling design and statistical analysis based on kriging spatial interpolation.
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