LEADER 02126nam  2200361za 450 
001 9910830444603321
005 20230721005755.0
010   $a9780470611463 (e-book)
010   $a9781848210608 (hbk.)
035   $a(MiAaPQ)EBC477673
035   $a(EXLCZ)992550000000005885
100   $a20080408d2008      uy             0
101 0 $aeng
135   $aur|n|nnn|||||
200 00$aAdvanced mapping of environmental data$b[electronic resource] $egeostatistics, machine learning, and Bayesian maximum entropy /$fedited by Mikhail Kanevski
210   $aLondon $cISTE ;$aHoboken, N.J. $cWiley$d2008
215   $a1 online resource (xiii, 313 p.) $cill
225 1 $aISTE ;$vv.62
320   $aIncludes bibliographical references and index.
327   $aChapter 1. Advanced Mapping of Environmental Data: Introduction -- Chapter 2. Environmental Monitoring Network Characterization and Clustering -- Chapter 3. Geostatistics: Spatial Predictions and Simulations -- Chapter 4. Spatial Data Analysis and Mapping Using Machine Learning Algorithms -- Chapter 5. Advanced Mapping of Environmental Spatial Data: Case Studies -- Chapter 6. Bayesian Maximum Entropy ? BME -- Index.
330   $aThis book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.
410  0$aISTE.
606   $aGeology$xStatistical methods
606   $aMachine learning
606   $aBayesian statistical decision theory
615  0$aGeology$xStatistical methods.
615  0$aMachine learning.
615  0$aBayesian statistical decision theory.
676   $a550.1519542
701   $aKanevski$b Mikhail$01701177
912   $a9910830444603321
996   $aAdvanced mapping of environmental data$94084742
997   $aUNINA