Forecasting rock slope failure: how reliable and effective are warning systems?

Abstract

Warning systems are increasingly applied to reduce damage caused by different magnitudes of rockslides and rockfalls. In an integrated risk-management approach, the optimal risk mitigation strategy is identified by comparing the achieved effectiveness and cost; estimating the reliability of the warning system is the basis for such considerations. Here, we calculate the reliability and effectiveness of the warning system installed in Preonzo prior to a major rockfall in May 2012. BReliability^is defined as the ability of the warning system to forecast the hazard event and to prevent damage. To be cost-effective, the warning system should forecast an event with a limited number of false alarms to avoid unnecessary costs for intervention measures. The analysis shows that to be reliable, warning systems should be designed as fail-safe constructions. They should incorporate components with low failure probabilities, high redundancy, have low warning thresholds, and additional control systems. In addition, the experts operating the warning system should have limited risk tolerance. In an additional hypothetical probabilistic analysis, we investigate the effect of the risk attitude of the decision makers and of the number of sensors on the probability of detecting the event and initiating a timely evacuation, as well as on the related intervention cost. The analysis demonstrates that quantitative assessments can support the identification of optimal warning system designs and decision criteria.

Quantifying the effectiveness of early warning systems for natural hazards

“…These example systems have been investigated in detailed case studies previously (Sättele et al, 2015a;Sättele et al, 2015b) and key results of these case studies are used in Sect. 3 to demonstrate individual steps of the proposed framework approach.…”

“…In the following, the three parts of the framework are summarized and individual steps are demonstrated with results of the two case studies Illgraben and Preonzo (Sättele et al, 2015a;Sättele et al, 2015b).…”

“…In April, sensor forecasts made with the inverse velocity model vary strongly among different sensors. On May 14, 10 out of 12 sensors predict the event correctly for the next day (Sättele et al, 2015b). terhall et al, 2013).…”

“…To quantify the human decision-maker, two risk types are specified with different evacuation criteria (Sättele et al, 2015b The vulnerability v (w) ij is reduced if the EWS sends timely information that leads to temporary measures, which decrease the susceptibility of objects to damage. If storm events are announced in a timely way, movable objects can be fixed; if flood warnings are issued, protective temporary measures such as sandbags or wooden barriers can be installed.…”

“…The first BN, which models both the technical and the inherent reliability of a EWS, is described for a debris flow EWS in Sättele et al (2015a). In a subsequent case study, the reliability of a partly automated rockslide warning system is assessed (Sättele et al, 2015b). The automated part is again modelled in a BN and human decision procedures of the non-automated part are assessed through a Monte Carlo analysis.…”