Item type | Current location | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|
Books | 006.312 POU (Browse shelf) | Available | 034888 |
006.312 ORE Big data now | 006.312 PAL Pattern recognition algorithms for data mining | 006.312 PHI Mathematical foundations for data analysis | 006.312 POU Natural hazards GIS-based spatial modeling using data mining techniques | 006.312 PRO Data science for business | 006.312 ROD Information-theoretic methods in data science | 006.312 ROG Advanced data science and analytics with python |
Includes bibliographical references
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 of 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.
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