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NH3.7

Prediction and forecasting of landslides
Convener: Filippo Catani  | Co-Conveners: Fausto Guzzetti , Nejan Huvaj 
Oral Programme
 / Tue, 05 Apr, 08:30–10:00  / Room 1
Poster Programme
 / Attendance Tue, 05 Apr, 17:30–19:00  / Display Tue, 05 Apr, 08:00–19:30  / Halls X/Y
Poster Summaries & DiscussionsPSD98 

Landslides are ubiquitous geomorphological phenomena with potentially catastrophic consequences. In several countries landslide mortality can be higher than that of any other natural hazard. Predicting landslides is a difficult task that is of both scientific interest and societal relevance that may help save lives and protect individual properties and collective resources. The session focuses on innovative methods and techniques to predict landslide occurrence, including the location, time, size, destructiveness of individual and multiple slope failures. All landslide types are considered, from fast rockfalls to rapid debris flows, from slow slides to very rapid rock avalanches. All geographical scales are considered, from the local to the global scale. Of interest are contributions investigating theoretical aspects of natural hazard prediction, with emphasis on landslide forecasting, including conceptual, mathematical, physical, statistical, numerical and computational problems, and applied contributions demonstrating, with examples, the possibility or the lack of a possibility to predict individual or multiple landslides, or specific landslide characteristics. Of particular interest are contributions aimed at: the evaluation of the quality of landslide forecasts; the comparison of the performance of different forecasting models; the use of landslide forecasts in operational systems; and investigations of the potential for the exploitation of new or emerging technologies e.g., monitoring, computational, Earth observation technologies, in order to improve our ability to predict landslides. We anticipate that the most relevant contributions will be collected in the special issue of an international journal.