A Heterogeneous Sampling Strategy to Model Earthquake-Triggered Landslides

Hui Yang; Peijun Shi; Duncan Quincey; Wenwen Qi; Wentao Yang

doi:10.1007/s13753-023-00489-8

Volume 14 Issue 4

Sep. 2023

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Hui Yang, Peijun Shi, Duncan Quincey, Wenwen Qi, Wentao Yang. A Heterogeneous Sampling Strategy to Model Earthquake-Triggered Landslides[J]. International Journal of Disaster Risk Science, 2023, 14(4): 636-648. doi: 10.1007/s13753-023-00489-8

Citation:

Hui Yang, Peijun Shi, Duncan Quincey, Wenwen Qi, Wentao Yang. A Heterogeneous Sampling Strategy to Model Earthquake-Triggered Landslides[J]. International Journal of Disaster Risk Science, 2023, 14(4): 636-648. doi: 10.1007/s13753-023-00489-8

Citation:

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A Heterogeneous Sampling Strategy to Model Earthquake-Triggered Landslides

doi: 10.1007/s13753-023-00489-8

Hui Yang¹,
Peijun Shi^2,3,4,
Duncan Quincey⁵,
Wenwen Qi⁶,
Wentao Yang^{1,5,7
,
,}

1. School of Soil and Water Conservation, Beijing Forestry University, Beijing, 100083, China;
2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China;
3. Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing Normal University, Beijing, 100875, China;
4. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China;
5. School of Geography, University of Leeds, Leeds, LS2 9JT, UK;
6. National Institute of Natural Hazards, Beijing, 100085, China;
7. Academy of Plateau Science and Sustainability, People's Government of Qinghai Province and Beijing Normal University, Xining, 810016, China

Funds:

This study was jointly supported by the Fundamental Research Funds for the Central Universities (Grant No.2021ZY46) and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No. 2019QZKK0906). Wentao Yang is grateful for the scholarship from the China Scholarships Council (No. 202006515016)

Accepted Date: 2023-04-02
Publish Date: 2023-05-22

Abstract

Abstract

Regional modeling of landslide hazards is an essential tool for the assessment and management of risk in mountain environments. Previous studies that have focused on modeling earthquake-triggered landslides report high prediction accuracies. However, it is common to use a validation strategy with an equal number of landslide and non-landslide samples, scattered homogeneously across the study area. Consequently, there are overestimations in the epicenter area, and the spatial pattern of modeled locations does not agree well with real events. In order to improve landslide hazard mapping, we proposed a spatially heterogeneous non-landslide sampling strategy by considering local ratios of landslide to non-landslide area. Coseismic landslides triggered by the 2008 Wenchuan Earthquake on the eastern Tibetan Plateau were used as an example. To assess the performance of the new strategy, we trained two random forest models that shared the same hyperparameters. The first was trained using samples from the new heterogeneous strategy, and the second used the traditional approach. In each case the spatial match between modeled and measured (interpreted) landslides was examined by scatterplot, with a 2 km-by-2 km fishnet. Although the traditional approach achieved higher AUC_ROC (0.95) accuracy than the proposed one (0.85), the coefficient of determination (R²) for the new strategy (0.88) was much higher than for the traditional strategy (0.55). Our results indicate that the proposed strategy outperforms the traditional one when comparing against landslide inventory data. Our work demonstrates that higher prediction accuracies in landslide hazard modeling may be deceptive, and validation of the modeled spatial pattern should be prioritized. The proposed method may also be used to improve the mapping of precipitation-induced landslides. Application of the proposed strategy could benefit precise assessment of landslide risks in mountain environments.
- Earthquake-triggered landslides,
- Landslide hazard modeling,
- Machine learning,
- Model validation,
- Sampling strategy,
- Tibetan Plateau

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References(44)

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