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The 100×120 km computational domain was discretized horizontally using a nested grid system consisting of three overlapping grids.
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θ was estimated using a nested grid-search algorithm written in ANSI C. The three-parameter 1-D variogram model described above was fitted manually to the spatial variogram estimated from the global space-time sample variogram as described earlier 4.2(1–2) and the resulting parameter set was used as starting values to initialise the algorithm.
In this work, the local grid refinement procedure is focused by using a nested Cartesian grid formulation.
In the coastal area, we use a nesting grid system with modern digital bathymetry and topography data with grid sizes of 1350, 450, 150 and 50 m provided by the Central Disaster Management Council.
The experimental design consisted of a nested grid with three scales, 1 × 1-m, 5 × 5-m and 20 × 20-m, using 182 neutron access tubes.
In this study, a nested grid SWAN model and a hybrid approach combining artificial neural network (ANN) and coarse grid SWAN modeling results are used to hindcast the significant wave height in two nearshore locations in the Persian Gulf.
The measurement design consisted of a nested grid with five transects.
Tuning of hyperparameters for RF (number of variables used to build a single tree, mtry) and PLS-DA (number of dimensions, K, and/or sparsness parameter, η) was done using a nested cross-validation scheme, comprised of stratified and repeated 10×5-fold resampling (inner loop) combined to a search grid of length 10 for the hyperparameters (outer loop).
Using a nested design without temporal replication, replicate core samples were taken from sampling stations nested within lagoons that were nested within catchment type.
Env was amplified using a nested PCR.
We used a nested case-control design.
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