250 pages | 8 Color Illus. | 52 B/W Illus.
Unexpected and extreme natural hazards resulting from the Earth's natural processes can be harmful to humans. As a result, powerful approaches have been developed to apply advanced machine learning and big data methods for extracting relevant patterns, high performance computing, and data visualization to the field of natural hazards. Machine Learning for Natural Hazards shares recent advances in the field, with emphasis on issues addressed by advanced machine learning and big data analytic techniques. This book aims to provide practitioners with efficient and effective tools to deal with natural hazard related data. Relevant, illustrative, study cases are also presented and discussed.
Advanced machine learning techniques for natural hazards. Support Vector Machines. Least Squared Support Vector Machines. Relevant vector machines. Extreme Learning Machine. Minimax Probability Machine. Advanced Neural networks. Logistic Regression. Genetic programming. Random Forests. Advanced Decision trees. Fuzzy. Meta-Heuristic Optimization. Deep learning. Big data.