Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics methods for variography and spatial predictions, approaches to conditional stochastic simulation and local probability distribution function estimation, and select aspects of Geographical Information Systems. It includes real case studies using Geostat Office software tools under MS Windows and also provides tools and methods to solve problems in prediction, characterization, optimization, and density estimation. The author describes fundamental methodological aspects of the analysis and modelling of spatially distributed data and the application by way of a specific and user-friendly software, GSO Geostat Office. Presenting complete coverage of geostatistics and machine learning algorithms, the book explores the relationships and complementary nature of both approaches and illustrates them with environmental and pollution data. The book includes introductory chapters on machine learning, artificial neural networks of different architectures, and support vector machines algorithms. Several chapters cover monitoring network analysis, artificial neural networks, support vector machines, and simulations. The book demonstrates thepromising results of the application of SVM to environmental and pollution data.

An integrated approach to environmental data management is necessitated by the complexity of the environmental problems that need to be addresses, coupled with the interdisciplinary approach that needs to be adopted to solve them. Agenda 21 of the Rio Environmental Conference mandated international programmes and organizations to take steps to develop common data and information management plans, and steps have been taken in this direction. The key word that defines the framework of the present book is `integration'. The book establishes the basics of integrated approaches and covers environmental data management systems within that framework, covering all aspects of data management, from objectives and constraints, design of data collection networks, statistical and physical sampling, remote sensing and GIS, databases, reliability of data, data analysis, and the transformation of data into information.