The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis

Diana Salciarini,Luca Ciabatta,Luca Brocca,Stefania Camici,Evelina Volpe,Elisabetta Cattoni

doi:10.3390/geosciences11080322

Abstract

The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate.

Highlights

In many areas of the world, rainfall-induced landslides represent a relevant threat to the population, infrastructure, buildings, and cultural heritage
It was shown that the definition of pdfs for the physical and mechanical properties of the soil influences the reliability analysis; this effect has been quantified with the exact method of Monte Carlo
The probability of failure was computed in relation to a particular case study where a detailed geotechnical characterization is available

Summary

Introduction

In many areas of the world, rainfall-induced landslides represent a relevant threat to the population, infrastructure, buildings, and cultural heritage. The most damaging landslides are triggered by intense or prolonged rainfall [6,7,8,9], and the most common phenomena are shallow landslides [10]. Landslide susceptibility (S) is the likelihood of a landslide occurring in an area on the basis of local terrain conditions [15]. It represents an estimate of “where” landslides are likely to occur

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