Abstract:
Computer-based landslides models are employed on large regions for the assessment
of slope failures. However, these models have specific operating framework
strategies for their configuration and input data with long processing time. To
address these concerns, a python-based slope stability model has been developed
with a modular framework that integrates parametrized geotechnical data and Digital
Elevation Model (DEM) to compute the Factor of Safety (FoS) and Failure
Probability (Pf). The model mechanics couples the classic infinite slope equation
based on Monte Carlo Simulations which allows generation of probability density
functions representing all random variables and probability of failure by repeated
calculations. For each node, multiple FoS and Pf values are computed and mean
values for each node provide predictive susceptibility. For a deeper insight elevation,
slope and aspect maps are likewise produced. The model was calibrated
and validated for a test site in Italy and a comparison was made with other opensource
models in terms of efficiency and accuracy. The developed model tested
with the upper and lower limits of parameterized dataset showed an accuracy in
the range of 79% to 88% and the results have shown that the developed model
provides a proportionate level of prediction accuracy and processing efficiency. A
component with the capability of spatial distribution of infiltration process would
further improve its accuracy.
