## Statistical Approaches for the Assessment of Landslide-Related Economic Losses

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##### Autor

Vega J.A.

Marín N.J.

Hidalgo C.A.

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In this paper, different statistical approaches are employed to consider several scenarios of hazard and infrastructure vulnerability to measure the magnitude of the mass movement and quantify the economic costs. In this context, we employ the techniques of simulation bootstrap, Monte-Carlo and variance reduction in the context of Monte-Carlo in order to compare the values of the economic losses before a potential disaster in an objective, standardized and reproducible way. With the purpose to explain the proposed methodology, a case study located in one of the most landslide-prone zone of the city of Medellin-Colombia is analysed, comparing different structural scenarios for a total of 48444 exposed buildings. Also, different seismic scenarios of landslide hazard were considered, varying the horizontal acceleration (Ah) that can act as one of the triggers of the mass movement. The novel proposed methodology permits to obtain an estimation of the probable economic losses by a certain landslide, and also to get better assessments by reducing the uncertainty and compare the results between different statistical approaches, taking into account the uncertainty of the exposed building costs. It is important to mention that between the simulated scenarios the better results are shown in the bootstrap simulation and Monte-Carlo simulation with reduction of variance. The analyzed simulation methodologies provide a better estimation of economic losses for horizontal acceleration of ground between 0 g and 0.3 g. It is noticed an economic reduction of on the order of 7%, 14% and 21% in the structural scenarios 2, 3 and 4 respectively, in comparison with the current structural condition of the exposed buildings, in case of a landslide triggered by an earthquake with the maximum expected horizontal acceleration for the city. © 2019 Published under licence by IOP Publishing Ltd.

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- Indexados Scopus [1337]