Multiple Kernel Learning of Environmental Data. Case Study: Analysis and Mapping of Wind Fields
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
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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.