Multisensor Landslide Monitoring as a Challenge for Early Warning: From Process Based to Statistic Based Approaches

In the last decades, several approaches were proposed accounting for early warning systems to manage in real time the risks due to fast slope failures where important elements, such as structures, infrastructures and cultural heritage are exposed. The challenge of these approaches is to forecast the slope evolution, thus providing alert levels suitable for managing infrastructures in order to mitigate the landslide risk and reduce the “response” time for interventions. Two different strategies can be defined in this regard: an observation-based approach (OBA) and a process-based approach (PBA), this last one comprehensive of semi-empirical approaches (SEA) and a statistical-based one (SBA). At this aim, some experiments are being performed at different scales in the framework of technical applications, consulting activities and research projects managed by the Research Centre for the Geological Risk (CERI) of the University of Rome “Sapienza”. These experiments are testing different kind of sensors including interferometers, optical cams connected to Artificial Intelligence (AI) systems, extensometers, distantiometers, rock-thermometers, for detecting changes in rock properties and detecting stress-strain changes, as well as pluviometers, anemometers, hygrometers, air-thermometers, micro- or nano- accelerometers and piezometers for detecting possible triggers. The results obtained up to now encourage improving the SBA, based on data clouding, and testing them more extensively, at a national scale, by selecting test sites for experiencing their suitability for intervention strategies/procedures. These test sites will be selected along railways or roadways (in co-operation with the responsible National Agencies) where man-cut trenches could predispose to rock slides or falls that involve the infrastructures, in order to experience the suitability of SBA versus OBA approaches for early warning in the framework of lifelines management.