Machine Learning for Spatial Environmental Data Theory, Applications, and Software

This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well.The authors describe new trends in machine learning and their application to spatial data. The text also includes real case studies based on environmental and pollution data. It includes a CD-ROM with software that will allow both students and researchers to put the concepts to practice.

PREFACE LEARNING FROM GEOSPATIAL DATA Problems and important concepts of machine learning Machine learning algorithms for geospatial data Contents of the book Software description Short review of the literature EXPLORATORY SPATIAL DATA ANALYSIS PRESENTATION OF DATA AND CASE STUDIES Exploratory spatial data analysis Data pre-processing Spatial correlations: Variography Presentation of data k-Nearest neighbours algorithm: a benchmark model for regression and classification Conclusions to chapter GEOSTATISTICS Spatial predictions Geostatistical conditional simulations Spatial classification Software Conclusions ARTIFICIAL NEURAL NETWORKS Introduction Radial basis function neural networks General regression neural networks Probabilistic neural networks Self-organising maps Gaussian mixture models and mixture density network Conclusions SUPPORT VECTOR MACHINES AND KERNEL METHODS Introduction to statistical learning theory Support vector classification Spatial data classification with SVM Support vector regression Advanced topics in kernel methods REFERENCES INDEX