This paper presents a review of methodology for semi-supervised modeling
with kernel methods, when the manifold assumption is guaranteed to
be satisfied. It concerns environmental data modeling on natural
manifolds, such as complex topographies of the mountainous regions,
where environmental processes are highly influenced by the relief.
These relations, possibly regionalized and nonlinear, can be modeled
from data with machine learning using the digital elevation models
in semi-supervised kernel methods. The range of the tools and methodological
issues discussed in the study includes feature selection and semisupervised
Support Vector algorithms. The real case study devoted to data-driven
modeling of meteorological fields illustrates the discussed approach.