Radioactive soil-contamination mapping and risk assessment is a vital
issue for decision makers. Traditional approaches for mapping the
spatial concentration of radionuclides employ various regression-based
models, which usually provide a single-value prediction realization
accompanied (in some cases) by estimation error. Such approaches do not
provide the capability for rigorous uncertainty quantification or
probabilistic mapping. Machine learning is a recent and fast-developing
approach based on learning patterns and information from data.
Artificial neural networks for prediction mapping have been especially
powerful in combination with spatial statistics. A data-driven approach
provides the opportunity to integrate additional relevant information
about spatial phenomena into a prediction model for more accurate
spatial estimates and associated uncertainty. Machine-learning
algorithms can also be used for a wider spectrum of problems than
before: classification, probability density estimation, and so forth.
Stochastic simulations are used to model spatial variability and
uncertainty. Unlike regression models, they provide multiple
realizations of a particular spatial pattern that allow uncertainty and
risk quantification. This paper reviews the most recent methods of
spatial data analysis, prediction, and risk mapping, based on machine
learning and stochastic simulations in comparison with more traditional
regression models. The radioactive fallout from the Chernobyl Nuclear
Power Plant accident is used to illustrate the application of the models
for prediction and classification problems. This fallout is a unique
case study that provides the challenging task of analyzing huge amounts
of data ('hard' direct measurements, as well as supplementary
information and expert estimates) and solving particular
decision-oriented problems.