Predictive Models for the Development of Landslide Early Warning Systems
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Predictive Models for the Development of Landslide Early Warning Systems details advanced techniques in implementing landslide early warning systems (LEWS). It provides a comprehensive resource for practitioners including different techniques and models in landslide early warning, their practical applications, and case studies. The modelling theory is provided in a detailed but succinct format, verified with on-site models for specific regions and scenarios for different types of landslides and triggering factors. The book covers four main topics required for an efficient LEWS: monitoring, inc...
Predictive Models for the Development of Landslide Early Warning Systems details advanced techniques in implementing landslide early warning systems (LEWS). It provides a comprehensive resource for practitioners including different techniques and models in landslide early warning, their practical applications, and case studies. The modelling theory is provided in a detailed but succinct format, verified with on-site models for specific regions and scenarios for different types of landslides and triggering factors. The book covers four main topics required for an efficient LEWS: monitoring, including data acquisition, transmission and maintenance of the instruments; analysis and forecasting, including forecasting methods; warning/dissemination of understandable messages alerting for the impending threat; and response. The exportability of different models is discussed in detail and followed by practical demonstrations for expert researchers' as well as postgraduates' needs. Predictive Models for the Development of Landslide Early Warning Systems offers in-depth, up-to-date best practices for implementing LEWS, based on current effective systems, new technologies, and standard methodologies at global level. This allows readers to understand universal LEWS methodologies and applications for use in their own research and development, including the use of current practical Big Data techniques.
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