Temporal Modeling of Rainfall-Triggered Landslides: A Hybrid Approach Combining Physically-Based Modeling and Extreme Value Analysis
Temporal Modeling of Rainfall-Triggered Landslides: A Hybrid Approach Combining Physically-Based Modeling and Extreme Value Analysis
Climate change induced the rise of extreme rainfall, resulting in an increase in the frequency and
magnitude of landslides. Hence, a novel temporal modeling of rainfall-induced landslides
incorporating both the dynamic nature of rainfall patterns and the slope failure mechanism was
proposed. The proposed approach consists of three steps: (1) analysis of a critical continuous
rainfall (CCR) using a physical-based model, (2) obtaining the cumulative distribution function of
generalized extreme value distribution via the annual maximum rainfall series, and (3) analysis of
temporal probability map. The result of the CCR map was validated with the 2018 landslide event
in a small area of Hiroshima Prefecture, Japan. The result shows that the CCR map is highly
reliable, with an AUC of 71.3%. The proportion of temporal probability >0.5 under the
nonstationary model is greater than approximately 1.7, 1.9, 2.0, and 2.3 times the stationary
model for the periods of 5, 10, 20, and 50 years, respectively. This indicates that the temporal
probability increases according to a longer time period due to climate change-induced increased
trend of extreme rainfall. The proposed approach can also be utilized to obtain the landslide
temporal probability map for areas lacking landslide inventory
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