The algorithmic approach to data modelling has developed rapidly these
last years, in particular methods based on data mining and machine
learning have been used in a growing number of applications. These
methods follow a data-driven methodology, aiming at providing the
best possible generalization and predictive abilities instead of
concentrating on the properties of the data model. One of the most
successful groups of such methods is known as Support Vector algorithms.
Following the fruitful developments in applying Support Vector algorithms
to spatial data, this paper introduces a new extension of the traditional
support vector regression (SVR) algorithm. This extension allows
for the simultaneous modelling of environmental data at several spatial
scales. The joint influence of environmental processes presenting
different patterns at different scales is here learned automatically
from data, providing the optimum mixture of short and large-scale
models. The method is adaptive to the spatial scale of the data.
With this advantage, it can provide efficient means to model local
anomalies that may typically arise in situations at an early phase
of an environmental emergency. However, the proposed approach still
requires some prior knowledge on the possible existence of such short-scale
patterns. This is a possible limitation of the method for its implementation
in early warning systems. The purpose of this paper is to present
the multi-scale SVR model and to illustrate its use with an application
to the mapping of Cs137 activity given the measurements taken in
the region of Briansk following the Chernobyl accident.