Abstract
The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards.
Highlights
To prevent or reduce damage, injuries, and death caused by landslides, there is a need for landslide hazard analysis, which estimates the probability of a potential landslide occurrence within a given period of time and over a specific area [1,2]
The maximum 24 h rainfall value for each year was calculated from hourly rainfall data and designated as the annual maximum (AM) rainfall for that year
We proposed a process to estimate temporal landslide probability using extreme
Summary
Statistical approaches acquire knowledge of susceptibility obtained through the statistical analysis of the relationship between landslide occurrences and various conditioning factors [5,7–13]. Statistical analysis considers the statistical relationship between landslides and conditioning factors exclusively without the consideration of slope failure mechanisms [14]. In recent years, physically based analysis, which incorporates the physical processes and mechanisms of landscape occurrence, has been used with a physical slope model to estimate the spatial probability of landslide occurrence independent of its occurrence history [14–32].
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