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Advanced Mapping of Environmental Data combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, Advanced Mapping of Environmental Data covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.
1. Model dependent (geostatistics) and data driven (machine learning algorithms)
2. Environmental spatial data.Monitoring networks quantification. Spatial patterns
3. Geostatistics. Spatial predictions and simulations. Linear models. Family of kriging models with illustrations. Nonlinear models. Risk mapping. Indicator kriging. Conditional stochastic simulations. Descriptions of spatial uncertainty and variability
4. Machine learning algorithms. Principles of learning. Learning from environmental spatial data. Posing of classification and regression problems. Artificial neural networks (ANN) for spatial data. Basic ANN models (theory and illustrative examples). Statistical learning theory for spatial data. Concepts and examples
5. Case studies: Geostatistics and machine learning. Classification problems. Regression problems
6. Bayesian maximum entropy (BME)
Mikhail Kanevski, Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland
"It gives a good overview, is clearly written, is concise, and includes many references to papers published in the different areas."
- Zentralblatt MATH, 2011