This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference 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.... and techniques described in aNeural Networks for Pattern Recognitiona by Christopher M. Bishop, (Oxford University ... The tutorial on the library, mpJ/ wwwtorch.ch/matos/tutorial.jfi, presents TORCH as a machine learning library, written in C++, and distributed under a BSD license. ... and generates meaningful problem solutions that allow an informative representation of the realworld system usinganbsp;...

Title | : | Advanced Mapping of Environmental Data |

Author | : | Mikhail Kanevski |

Publisher | : | John Wiley & Sons - 2013-05-10 |

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