دانلود کتاب Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

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.Content: Chapter 1 Advanced Mapping of Environmental Data: Introduction (pages 1–17): M. KanevskiChapter 2 Environmental Monitoring Network Characterization and Clustering (pages 19–46): D. Tuia and M. KanevskiChapter 3 Geostatistics: Spatial Predictions and Simulations (pages 47–94): E. Savelieva, V. Demyanov and M. MaignanChapter 4 Spatial Data Analysis and Mapping Using Machine Learning Algorithms (pages 95–148): F. Ratle, A. Pozdnoukhov, V. Demyanov, V. Timonin and E. SavelievaChapter 5 Advanced Mapping of Environmental Spatial Data: Case Studies (pages 149–246): L. Foresti, A. Pozdnoukhov, M. Kanevski, V. Timonin, E. Savelieva, C. Kaiser, R. Tapia and R. PurvesChapter 6 Bayesian Maximum Entropy — BME (pages 247–306): G. Christakos