A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale
Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (<i>F</i><sub>s</sub>) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-03-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://www.nat-hazards-earth-syst-sci.net/18/969/2018/nhess-18-969-2018.pdf |
| Summary: | Conventional outputs of physics-based landslide
forecasting models are presented as deterministic warnings by calculating
the safety factor (<i>F</i><sub>s</sub>) of potentially dangerous slopes. However, these models
are highly dependent on variables such as cohesion force and internal
friction angle which are affected by a high degree of uncertainty especially
at a regional scale, resulting in unacceptable uncertainties of <i>F</i><sub>s</sub>. Under
such circumstances, the outputs of physical models are more suitable if
presented in the form of landslide probability values. In order to develop
such models, a method to link the uncertainty of soil parameter values with
landslide probability is devised. This paper proposes the use of Monte Carlo
methods to quantitatively express uncertainty by assigning random values to
physical variables inside a defined interval. The inequality <i>F</i><sub>s</sub> < 1 is
tested for each pixel in <i>n</i> simulations which are integrated in a unique
parameter. This parameter links the landslide probability to the
uncertainties of soil mechanical parameters and is used to create a
physics-based probabilistic forecasting model for rainfall-induced shallow
landslides. The prediction ability of this model was tested in a case study,
in which simulated forecasting of landslide disasters associated with heavy
rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan
province, China, was performed. The proposed model successfully forecasted
landslides in 159 of the 176 disaster points registered by the
geo-environmental monitoring station of Sichuan province. Such testing
results indicate that the new model can be operated in a highly efficient way
and show more reliable results, attributable to its high prediction accuracy.
Accordingly, the new model can be potentially packaged into a forecasting
system for shallow landslides providing technological support for the
mitigation of these disasters at regional scale. |
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| ISSN: | 1561-8633 1684-9981 |