This paper presents general problems and approaches for the spatial
data analysis using machine learning algorithms. Machine learning
is a very powerful approach to adaptive data analysis, modelling
and visualisation. The key feature of the machine learning algorithms
is that they learn from empirical data and can be used in cases when
the modelled environmental phenomena are hidden, nonlinear, noisy
and highly variable in space and in time. Most of the machines learning
algorithms are universal and adaptive modelling tools developed to
solve basic problems of learning from data: classification/pattern
recognition, regression/mapping and probability density modelling.
In the present report some of the widely used machine learning algorithms,
namely artificial neural networks (ANN) of different architectures
and Support Vector Machines (SVM), are adapted to the problems of
the analysis and modelling of geo-spatial data. Machine learning
algorithms have an important advantage over traditional models of
spatial statistics when problems are considered in a high dimensional
geo-feature spaces, when the dimension of space exceeds 5. Such features
are usually generated, for example, from digital elevation models,
remote sensing images, etc. An important extension of models concerns
considering of real space constrains like geomorphology, networks,
and other natural structures. Recent developments in semi-supervised
learning can improve modelling of environmental phenomena taking
into account on geo-manifolds. An important part of the study deals
with the analysis of relevant variables and models' inputs. This
problem is approached by using different feature selection/feature
extraction nonlinear tools.
To demonstrate the application of machine learning algorithms several
interesting case studies are considered: digital soil mapping using
SVM, automatic mapping of soil and water system pollution using ANN;
natural hazards risk analysis (avalanches, landslides), assessments
of renewable resources (wind fields) with SVM and ANN models, etc.
The dimensionality of spaces considered varies from 2 to more than
30.
Figures 1, 2, 3 demonstrate some results of the studies and their
outputs.
Finally, the results of environmental mapping are discussed and compared
with traditional models of geostatistics.