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.
|Citazione:||Ballabio, C. (2009). Spatial prediction of soil properties in temperate mountain regions using support vector regression. GEODERMA, 151(3-4), 338-350.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Titolo:||Spatial prediction of soil properties in temperate mountain regions using support vector regression|
|Data di pubblicazione:||23-mag-2009|
|Digital Object Identifier (DOI):||10.1016/j.geoderma.2009.04.022|
|Appare nelle tipologie:||01 - Articolo su rivista|