## Abstract

As the frequency and intensity of heavy rainfall increase, the frequency of extreme rainfall-induced landslides also increases. Thus, the importance of accurate assessment of extreme rainfall-induced landslide hazard increases. Landslide hazard assessment requires estimations of two components: spatial probability and temporal probability. While various approaches have been successfully used to estimate spatial landslide susceptibility, fewer studies have addressed temporal probability and, consequently, a commonly accepted method does not exist. Prior approaches have estimated temporal probability using frequency analysis of past landslides or landslide triggering rainfall events. Hence, a large amount of information was required: sufficiently complete historical data on recurrent landslides and repetitive rainfall events. However, in many cases, it is difficult to obtain such complete historical data. Therefore, this study developed a new approach that can be applied to an area where incomplete data are available or where nonrepetitive landslide events have occurred. To evaluate the temporal probability of landslide occurrence, the developed approach adopted extreme value analysis using the Gumbel distribution. The exceedance probability of a rainfall threshold was evaluated, using the Gumbel model, with 72-h antecedent rainfall threshold. This probability was then considered to be the temporal probability of landslide occurrence. The temporal probability of landslides was then integrated with landslide susceptibility results from a multi-layer perceptron model. Consequently, the landslide hazards for different future time periods, from 1 to 200 years, were estimated.

Original language | English |
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Pages (from-to) | 321-338 |

Number of pages | 18 |

Journal | Landslides |

Volume | 18 |

Issue number | 1 |

DOIs | |

Publication status | Published - 2021 Jan |

### Bibliographical note

Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1058063).

Publisher Copyright:

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

## All Science Journal Classification (ASJC) codes

- Geotechnical Engineering and Engineering Geology