Machine Learning for Spatial Environmental Data

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