Active Learning for Monitoring Network Optimization

Kernel-based active learning strategies were studied for the optimization of environmental monitoring networks. This chapter introduces the basic machine learning algorithms originated in the statistical learning theory of Vapnik (1998). Active learning is closer to an optimization done using sequential Gaussian simulations. The chapter presents the general ideas of statistical learning from data. It derives the basics of kernel-based support vector algorithms. The active learning framework is presented and machine learning extensions for active learning are described in the chapter. Kernel-based active learning strategies are tested on real case studies. The chapter explores the use of a committee of machines to characterize the effect of sampling uncertainty to predict the uncertainty. Future studies have to be targeted towards the development of multi-objective monitoring network design algorithms to measure complex, nonlinear and high-dimensional environmental processes that vary in space and time.