Digital soil mapping in mountain areas faces two major limitations: the small number of available observations and the non-linearity of the relations between environmental variables and soil properties. A possible approach to deal with these limitations involves the use of non-parametric models to interpolate soil properties of interest. Among the different approaches currently available, Support Vector Regression (SVR) seems to have several advantages over other techniques. SVR is a set of techniques in which model complexity is limited by the learning algorithm itself, which prevents overfitting. Moreover, the non-linear approximation of SVR is based on a kernel transformation of the data, which avoids the use of complex functions and is computationally feasible; while the resulting projection in feature space is especially suited for sparse datasets. A brief introduction to this methodology, a comparison with other popular methodologies and a framework for the application of this approach to a study site in the Italian Alps is discussed.

Ballabio, C. (2009). Spatial prediction of soil properties in temperate mountain regions using support vector regression. GEODERMA, 151(3-4), 338-350 [10.1016/j.geoderma.2009.04.022].

Spatial prediction of soil properties in temperate mountain regions using support vector regression

BALLABIO, CRISTIANO
2009

Abstract

Digital soil mapping in mountain areas faces two major limitations: the small number of available observations and the non-linearity of the relations between environmental variables and soil properties. A possible approach to deal with these limitations involves the use of non-parametric models to interpolate soil properties of interest. Among the different approaches currently available, Support Vector Regression (SVR) seems to have several advantages over other techniques. SVR is a set of techniques in which model complexity is limited by the learning algorithm itself, which prevents overfitting. Moreover, the non-linear approximation of SVR is based on a kernel transformation of the data, which avoids the use of complex functions and is computationally feasible; while the resulting projection in feature space is especially suited for sparse datasets. A brief introduction to this methodology, a comparison with other popular methodologies and a framework for the application of this approach to a study site in the Italian Alps is discussed.
Articolo in rivista - Articolo scientifico
Digital soil mapping; Mountain regions; Support vector regression; Model comparison
English
Ballabio, C. (2009). Spatial prediction of soil properties in temperate mountain regions using support vector regression. GEODERMA, 151(3-4), 338-350 [10.1016/j.geoderma.2009.04.022].
Ballabio, C
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/5949
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