Machine Learning for Spatial Environmental Data

Theory, Applications and Software

Mikhail Kanevski, Alexei Pozdnoukhov, Vadim Timonin

• Publisher: PPUR
• Year: 2009
• Print ISBN: 9782940222247
• eBook ISBN: 9782889149582

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Table of contents

• PREFACE
• TABLE OF CONTENTS
• 1 LEARNING FROM GEOSPATIAL DATA
• 2 EXPLORATORY SPATIAL DATA ANALYSIS. PRESENTATION OF DATA AND CASE STUDIES
• 3 GEOSTATISTICS
• 4 ARTIFICIAL NEURAL NETWORKS
• 5 SUPPORT VECTOR MACHINES AND KERNEL METHODS
• REFERENCES
• INDEX