Abstract
Given the increased frequency of extreme rainfall events, pre-disaster countermeasures against landslides triggered by heavy rainfall are important to enhance disaster resilience. This study presents a methodology for economic risk assessment of structures affected by rainfall-induced landslides using machine learning (ML). Random Forest and LightGBM algorithms were applied to develop ML-based landslide prediction models considering the spatial distributions of landslide conditioning and triggering factors. The rainfall index was calculated considering the temporal variation in rainfall and was used as a feature associated with rainfall intensity. The rainfall hazard curve, representing the relationship between the rainfall index and its annual exceedance probability, was statistically estimated using a generalised extreme value distribution. Rainfall-induced landslide susceptibility was assessed using an ML-based landslide prediction model and rainfall hazard curve. Finally, the risk curve associated with the economic loss from structures damaged by rainfall-induced landslides was estimated based on landslide susceptibility and structure distribution maps. In this study, LightGBM showed better prediction performance for evaluating rainfall-induced landslide susceptibility than Random Forest. An illustrative example is presented to demonstrate that the proposed methodology can be used to develop an appropriate risk-based disaster mitigation strategy.
Original language | English |
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Pages (from-to) | 228-243 |
Number of pages | 16 |
Journal | Georisk |
Volume | 18 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- Machine learning
- disaster mitigation strategy
- economic risk
- landslide susceptibility
- rainfall hazard