Recently, kernel-based Machine Learning methods have gained great
popularity in many data analysis and data mining fields: pattern
recognition, biocomputing, speech and vision, engineering, remote
sensing etc. The paper describes the use of kernel methods to approach
the processing of large datasets from environmental monitoring networks.
Several typical problems of the environmental sciences and their
solutions provided by kernel-based methods are considered: classification
of categorical data (soil type classification), mapping of environmental
and pollution continuous information (pollution of soil by radionuclides),
mapping with auxiliary information (climatic data from Aral Sea region).
The promising developments, such as automatic emergency hot spot
detection and monitoring network optimization are discussed as well.