Advanced Mapping of Environmental Data/Geostatistics, Machine Learning and Bayesian Maximum Entropy (ISTE)
By: Mikhail Kanevski (editor)Hardback
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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.
Mikhail Kanevski , Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland.
Preface. Chapter 1. Advanced Mapping of Environmental Data: Introduction (M. KANEVSKI). 1.1. Introduction. 1.2. Environmental data analysis: problems and methodology. .1.3. Resources. .1.4. Conclusion. 1.5. References. Chapter 2. Environmental Monitoring Network Characterization and Clustering (D. TUIA and M. KANEVSKI). 2.1. Introduction. 2.2. Spatial clustering and its consequences. .2.3. Monitoring network quantification. .2.4. Validity domains. 2.5. Indoor radon in Switzerland: an example of a real monitoring network. .2.6. Conclusion. 2.7. References. Chapter 3. Geostatistics: Spatial Predictions and Simulations (E. SAVELIEVA, V. DEMYANOV and M. MAIGNAN). 3.1. Assumptions of geostatistics. 3.2. Family of kriging models. 3.3. Family of co-kriging models. 3.4. Probability mapping with indicator kriging. 3.5. Description of spatial uncertainty with conditional stochastic simulations. 3.6. References. Chapter 4. Spatial Data Analysis and Mapping Using Machine Learning Algorithms (F. RATLE, A. POZDNOUKHOV, V. DEMYANOV, V. TIMONIN and E. SAVELIEVA). 4.1. Introduction. 4.2. Machine learning: an overview. 4.3. Nearest neighbor methods. 4.4. Artificial neural network algorithms. 4.5. Statistical learning theory for spatial data: concepts and examples. 4.6. Conclusion. 4.7. References. Chapter 5. Advanced Mapping of Environmental Spatial Data: Case Studies (L. FORESTI, A. POZDNOUKHOV, M. KANEVSKI, V. TIMONIN, E. SAVELIEVA, C. KAISER, R. TAPIA and R. PURVES). 5.1. Introduction. 5.2. Air temperature modeling with machine learning algorithms and geostatistics. 5.3. Modeling of precipitation with machine learning and geostatistics. 5.4. Automatic mapping and classification of spatial data using machine learning. 5.5. Self-organizing maps for spatial data - case. 5.6. Indicator kriging and sequential Gaussian simulations for probability mapping. Indoor radon case study. 5.7. Natural hazards forecasting with support vector machines - case study: snow avalanches. 5.8. Conclusion. 5.9. References. Chapter 6. Bayesian Maximum Entropy - BME (G. CHRISTAKOS). 6.1. Conceptual framework. 6.2. Technical review of BME. 6.3. Spatiotemporal random field theory. 6.4. About BME. 6.5. A brief review of applications. 6.6. References. List of Authors. Index.
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