Keywords: monitoring network optimisation, machine learning, support vector machines, active learning, geostatistics, spatial data classification, climate data, environmental pollution, indicator kriging
Monitoring network optimisation for spatial data classification using support vector machines
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.