A Rainfall-Driven Virtual Sensors Model to preserve Landslide Early-Warning Capabilities under Monitoring Systems Failures.

Monitoring systems are essential tools for landslide risk mitigation. Monitoring reduces vulnerability by providing the data required for slope instability characterization and hazard assessment. Moreover, alert and alarm systems rely on the ability of monitoring networks to timely deliver reliable and precise measurements. Remote monitoring systems are not mature enough to ensure reliable early-warning capabilities; therefore, information redundancy is achieved by deploying a set of sensors directly on the landslide body. Commonly used devices include total stations, GPS receivers, automatic inclinometers, and ground-based radar systems. However, the harsh environmental conditions typical of unstable slopes frequently affect the instruments performance and the data availability. The lack of a stable power supply is one of the main limitations of these systems, often preventing their operation precisely during the most critical situations, such as thunderstorms. In addition, adverse atmospheric conditions, including fog or low cloud cover, can compromise the visibility of topographic benchmarks, reducing data availability when it is most needed. To address these limitations a nonlinear parametric model for forecasting landslide displacements based on rainfall input has been developed. The model is trained and continuously updated using displacement data acquired by the monitoring system. In the event of system failure, the model is able to simulate landslide kinematics by means of “virtual sensors”, forecasting displacements and detecting sudden accelerations of the landslide, thereby preserving the early-warning functionality. The approach was successfully tested using data from the monitoring system installed at the Rotolon landslide, located in the municipality of Recoaro Terme (Vicenza, Italy).