The paper presents a novel method for monitoring network optimisation,
based on a recent machine learning technique known as support vector
machine. It is problem-oriented in the sense that it directly answers
the question of whether the advised spatial location is important
for the classification model. The method can be used to increase
the accuracy of classification models by taking a small number of
additional measurements. Traditionally, network optimisation is performed
by means of the analysis of the kriging variances. The comparison
of the method with the traditional approach is presented on a real
case study with climate data.