Suchergebnisse

Abstract. Forecasting a catastrophic collapse is a key element in landslide risk reduction, but it is also a very difficult task owing to the scientific difficulties in predicting a complex natural event and also to the severe social repercussions caused by a false or missed alarm. A prediction is always affected by a certain error; however, when this error can imply evacuations or other severe consequences a high reliability in the forecast is, at least, desirable. In order to increase the confidence of predictions, a new methodology is presented here. In contrast to traditional approaches, this methodology iteratively applies several forecasting methods based on displacement data and, thanks to an innovative data representation, gives a valuation of the reliability of the prediction. This approach has been employed to back-analyse 15 landslide collapses. By introducing a predictability index, this study also contributes to the understanding of how geology and other factors influence the possibility of forecasting a slope failure. The results showed how kinematics, and all the factors influencing it, such as geomechanics, rainfall and other external agents, are key concerning landslide predictability.

Abstract. On 10 March 2010, because of the heavy rainfall in the preceding days, the Montaguto landslide (Southern Italy) reactivated, affecting both state road 90 Delle Puglie and the Rome–Bari railway. A similar event occurred on May 2005 and on September 2009. As a result, the National Civil Protection Department (DPC) started an accurate monitoring and analysis program. A monitoring project using the GB-InSAR (ground-based interferometric synthetic aperture radar) system was emplaced to investigate the landslide kinematics, plan urgent safety measures for risk mitigation and design long-term stabilization work.Here, we present the GB-InSAR monitoring system results and its applications in the observational method (OM) approach. GB-InSAR is an established instrument for long-term campaigns aimed at early warning and monitoring during construction works. Our paper further develops these aspects in that it highlights how the OM based on the GB-InSAR technique can produce savings in terms of cost and time in engineering projects without compromising safety. This study focuses on the key role played by the monitoring activities during the design and planning activities, with special reference to the emergency phase.

Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.