Volpe, E.; Ciabatta, L.; Salciarini, D.; Camici, S.; Cattoni, E.; Brocca, L. The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis. Geosciences2021, 11, 322.
Volpe, E.; Ciabatta, L.; Salciarini, D.; Camici, S.; Cattoni, E.; Brocca, L. The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis. Geosciences 2021, 11, 322.
Volpe, E.; Ciabatta, L.; Salciarini, D.; Camici, S.; Cattoni, E.; Brocca, L. The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis. Geosciences2021, 11, 322.
Volpe, E.; Ciabatta, L.; Salciarini, D.; Camici, S.; Cattoni, E.; Brocca, L. The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis. Geosciences 2021, 11, 322.
Abstract
The development of forecasting models for the evaluation of potential slope instability after rainfall event represents an important issue for the scientific community. This topic has received considerable impetus due to climate change effect on the territory [1, 2] as several studies demonstrate that the increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes [3]. 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.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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