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Using the dummy variable indicators, there was some indication of non-linearity, however.
Summarily, the results of this study indicated that a successful combination of OPS-based variable indicators and their subsequent incorporation into DRS-based chemometric models can potentially improve model predictions that can be further combined with geostatistical interpolation methods to produce spatial maps of soil properties.
The BayesC model included a set of variable indicators, d = { d1, …, d n }, which indicates if a SNP is (d i = 1) or is not (d i = 0) in the model.
Moreover, both PLSR and SVR optimal models used the identical best variable indicators.
The overall results of this study advocate the use of OPS approach with variable indicators and their combinations as a promising strategy to develop simple and effective DRS models for soils.
Results not only highlight reasonably good prediction, particularly with spatially variable indicators such as imperviousness across all case studies, but also logical contrasts and consistency in the chosen planning parameters across the different case study districts.
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Results indicated that the variable indicator-based SVR model yielded superior predictability relative to full-spectrum PLSR model for all soil parameters.
For Blantyre, Lilongwe and Mzimba districts the values of the asset indices are considerably higher than those of the single variable indicator.
This variable indicator suite was combined with an ordered predictor selection (OPS) method to choose the optimum number of spectral variables (NSV).
It is referred to as a simple or single variable indicator in this paper.
For 21 districts allocations based on the single variable indicator are higher.
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