This book describes the fundamental methodological aspects of the analysis and modelling of spacially distributed data, and the applications with the specific userfriendly software Geostat Office. The methods presented in this book include two domains of geostatistics and of machine learning algorithms, and some aspects of Geographical Information Systems. The geostatistical methods cover the traditionnal variography and spatial predictions, as well as an extensive part on conditional stochastic simulations and estimation of local probability distribution functions. A special chapter is devoted to the exploratory spatial data analysis, where the analysis of monitoring network is extensively decribed. In addition to more traditional geostatistics, the methods of artificial neural networks of different architectures ans Support Vector Machines (SVM) are explained ans illustrated. The key feature of machine learning algorithms is that learn from data and can be efficiently used when the modelled phenomenon is not described accurately. Machine Learning algorithms are adaptive tools to solve prediction, characterization, optimisation and density estimation problems. The fundamentals of Statistical Learning Theory (Vapnik-Chervonenkis theory) is explained using examples of real environmental spatial data; SVM develop robust data models with good generalisation capabilities.
The book is distributed with the student version of Geostat Office Software which runs under Microsoft Windows. The book and its GSO software can be useful for teaching as well as for modelling real case studies.
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The book consists in a paper-text (analysis of network monitoring, geostatistical method and the most advanced effective application of Artificial Neural Networks for mapping) and in a CD-ROM (paper-text in pdf format, with coloured display of all maps and graphs, “Geostat Office” software for Ms-Windows and user’s manual).

The book used with Geostat Office is useful for teaching as a tutorial text as well as for modelling real case studies.

Preface - Introduction to environmental data analysis and modelling - Exploratory spatial data analysis. Analysis of monitoring networks. Declustering - Spatial data analysis: deterministic interpolations - Introduction to Geostatistics. Variography - Geostatistical spatial predictions - Estimation of local probability density functions - Conditional stochastic simulations - Artificial neural networks and spatial data analysis - Support Vector Machines for environmental spatial data - Geographical Information Systems and spatial data analysis - Conclusions - Glossaries - References.

The book presents the state of the art in machine learning algorithms as applied to the classification and mapping of spatially distributed environmental data. 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.

Environment Geomechanics is a relatively new discipline at the interface between built and natural environment. Il es devoted to the understanding of the mechanical behavior of geomaterials under various environmental conditions. The new theories and models developed in this context will find applications in a large field of engineering.