Spatial Data Mapping with Support Vector Regression

The paper deals with the novel application of Support Vector Regression (SVR) for the analysis and modelling of spatially distributed environmental data. Mapping of soil pollution is considered as a real case study. Variography is widely used to control the performance of the machines. Geostatistical explanations for the SVR hyperparameters are given. Obtained results demonstrate flexibility and efficiency of SVR application to spatial data.

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