Silvia Franceschini

Implementation of a Probabilistic Model of Landslide Occurrence on a Civil Protection Alert System at Regional Scale

In this work we describe the deployment of a probabilistic model for landslide occurrence to support the decisional process within the Civil Protection Agency of the Emilia-Romagna Region (Italy). The model is based on the Bayes’ theorem and computes the conditional probability to have a landslide in response to a rainfall event of given duration and intensity. Unlike most the methods used to date to define rainfall thresholds, our probabilistic approach takes into account of the uncertainty in the data and provides a quantitative assessment of the threshold reliability. The model was recently applied to evaluate the landslide probability in the Emilia-Romagna region, taking advantage of the landslide historical archive that includes about 3,000 landslides whose date of occurrence is known with daily accuracy. For the operational use, we fitted the isolines of landslide probability on the rainfall duration-intensity plane and created two easy-to-read probability charts useful at two different scales: the rain gauge reference area (about 65 km2) and the regional alert zone (about 2,600 km2). The user combines the rainfall data recorded by the automated rain gauge network and the rainfall forecasts to characterize the rainfall event, and plot the rainfall path on the probability charts to evaluate the probability of failure. A sample application of the model is described.

Application of Persistent Scatterers Interferometry Time-Series Analysis (PS-Time) to Enhance the Radar Interpretation of Landslide Movements

In the last few years, the success of Persistent Scatterers Interferometry (PSI) techniques has largely increased because of the ever enhanced availability of space born radar data. Recently, various authors worked about using PSI techniques in order to better understand slope movements in general and in particular landslides kinematic. PS-time approach represents a new method for the automatic classification of PSI time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends, such as uncorrelated, linear, quadratic, bilinear and discontinuous, that describe different styles of ground deformations, so this new approach can be successfully used to improve the radar interpretation of land-slide movements. In this paper we present the results of the application of PS-time to two unstable areas in Northern Apennines of Italy (San Benedetto Val di Sambro, Province of Bologna; Vairo, Province of Parma). Results show that the time series analysis can greatly improve our understanding of the deformation phenomena, and pro-vide useful information in addition to the conventional analysis based on the mean velocity alone.