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Analysis and Modelling of Spatial Environmental Data
Mikhail Kanevski, University of Lausanne, Lausanne, Switzerland; Michel Maignan, University of Lausanne, Lausanne Switzerland
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Cat. #:  DK1218
ISBN:  9780824759810
ISBN 10:  0824759818
Publication Date:  March 30, 2004
Number of Pages:  300
Availability:  In Stock
Binding(s):  Hardback

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Features
  • Describes real case studies using Geostat Office software tools under MS Windows
  • Covers monitoring network analysis, artificial neural networks, support vector machines, and simulations
  • Provides tools and methods to solve problems in prediction, characterization, optimization, and density estimation
  • Includes a CD-ROM with a student version of Geostat Office software for analyzing, processing, and presenting spatially distributed data; it is fully functional with a restricted number of data points.

  • Summary
    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.