Automatic environmental monitoring networks enforced by wireless communication
technologies provide large and ever increasing volumes of data nowadays.
The use of this information in natural hazard research is an important
issue. Particularly useful for risk assessment and decision making
are the spatial maps of hazard-related parameters produced from point
observations and available auxiliary information. The purpose of
this article is to present and explore the appropriate tools to process
large amounts of available data and produce predictions at fine spatial
scales. These are the algorithms of machine learning, which are aimed
at non-parametric robust modelling of non-linear dependencies from
empirical data. The computational efficiency of the data-driven methods
allows producing the prediction maps in real time which makes them
superior to physical models for the operational use in risk assessment
and mitigation. Particularly, this situation encounters in spatial
prediction of climatic variables (topo-climatic mapping). In complex
topographies of the mountainous regions, the meteorological processes
are highly influenced by the relief. The article shows how these
relations, possibly regionalized and non-linear, can be modelled
from data using the information from digital elevation models. The
particular illustration of the developed methodology concerns the
mapping of temperatures (including the situations of Föhn and temperature
inversion) given the measurements taken from the Swiss meteorological
monitoring network. The range of the methods used in the study includes
data-driven feature selection, support vector algorithms and artificial
neural networks.