Using short-interval landslide inventories to build short-term and overall spatial prediction models for earthquake-triggered landslides based on machine learning for the 2018 Lombok earthquake sequence

Changhu Xue, Kejie Chen (), Hui Tang, Chaoqi Lin and Wenfeng Cui
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Changhu Xue: Southern University of Science and Technology
Kejie Chen: Southern University of Science and Technology
Hui Tang: GFZ German Research Centre for Geoscience
Chaoqi Lin: Southern University of Science and Technology
Wenfeng Cui: Southern University of Science and Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 114, issue 3, No 50, 3575-3595

Abstract: Abstract During an earthquake sequence, there are often multiple recurring landslides. Understanding the spatial distribution of the landslides triggered by the first earthquake can help us predict the landslide susceptibility for subsequent shakes over a short term. This study used two landslide inventories from the Lombok earthquake sequence in Indonesia in 2018 to construct a short-term secondary disaster prediction model and an overall spatial prediction model using four machine learning algorithms. The average accuracy of the positive samples predicted by the prediction model was 7.1% lower than that of the short-term model. The highest accuracy of the overall prediction model was 14.9% higher, on average, and the area under the ROC curve (AUC) score was 8.1% higher, on average, but the corresponding probability thresholds were lower. The reason for this difference is that, in the short-term prediction model, since most of the landslides in the first landslide inventory were prone to fail two or more times due to the effect of multiple earthquakes, the prediction results have a high positive rate. This feature of the short-term prediction model makes it suitable for landslide rescue guidance in a sequence of earthquakes. In contrast, the overall prediction model can better represent the spatial distribution of the earthquake-triggered landslides in the area.

Keywords: Earthquake-induced landslides; Machine learning; Landslide susceptibility analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05532-3

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