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This edited volume assesses capabilities of data mining algorithms for spatial modeling of natural hazards in different countries based on a collection of essays written by experts in the field. The book is organized on different hazards including landslides, flood, forest fire, land subsidence, earthquake, and gully erosion. Chapters were peer-reviewed by recognized scholars in the field of natural hazards research. Each chapter provides an overview on the topic, methods applied, and discusses examples used. The concepts and methods are explained at a level that allows undergraduates to understand and other readers learn through examples. This edited volume is shaped and structured to provide the reader with a comprehensive overview of all covered topics. It serves as a reference for researchers from different fields including land surveying, remote sensing, cartography, GIS, geophysics, geology, natural resources, and geography. It also serves as a guide for researchers, students, organizations, and decision makers active in land use planning and hazard management.

Data mining. --- Natural disasters --- Emergency management --- Geographic information systems. --- Geology. --- Natural Hazards. --- Data Mining and Knowledge Discovery. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Geognosy --- Geoscience --- Earth sciences --- Natural history --- Spatial data mining. --- Natural disasters. --- Natural calamities --- Disasters

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Geophysics --- Meteorology. Climatology --- Geology. Earth sciences --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- datamining --- remote sensing --- data mining --- geologie --- natuurrampen --- data acquisition