ABSTRACT
The LAMP (LAndslide Monitoring and Predicting) project proposes the use of a dense, low-cost and self-sufficient network of sensors, disseminated on the ground, coupled with a cognitive/predictive hydrological-geotechnical (IHG) model, to monitor and predict landslides triggered by rains. The sensor network monitors the local hydrogeological conditions. The proposed IHG model (Federici et al. 2014) analyzes, in real time, the propensity to collapse of different portions of the territory, by establishing a cause-and-effect relationship between rainfall and occurrence of the landslide. The LAMP’s final products are a map of landslide susceptibility in the occurrence of the real time rainfall and a map forecasting the susceptibility evolution in occurrence of the expected short term rain. The knowledge of the dynamics that trigger the phenomenon can indicate areas with high susceptibility to instability and can activate alarms before the phenomenon manifests itself, having real-time indication of susceptibility to instability, and forecast critical events potentially triggered by expected short term rain.
