Environmental Data Mapping with Support Vector Regression and Geostatistics

The paper presents decision-oriented mapping of pollution using hybrid models based on statistical learning theory (support vector regression or SVR) and spatial statistics (geostatistics). Adaptive and robust SVR approach is used to model non-linear large scale trends in the region and geostatistical models – spatial predictions and spatial simulations – are used to prepare decisionoriented maps: prediction maps along with maps of error variance and equiprobable digital models of the pollution based on conditional stochastic simulations. The quality of the proposed approach is tested with the validation data set not used for the model development. Real data on soil contamination by Chernobyl radionuclides in Russia is used as a case study.