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].
Citazione: | 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]. | |
Tipo: | Articolo in rivista - Articolo scientifico | |
Carattere della pubblicazione: | Scientifica | |
Titolo: | Spatial prediction of soil properties in temperate mountain regions using support vector regression | |
Autori: | Ballabio, C | |
Autori: | ||
Data di pubblicazione: | 23-mag-2009 | |
Lingua: | English | |
Rivista: | GEODERMA | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.geoderma.2009.04.022 | |
Appare nelle tipologie: | 01 - Articolo su rivista |