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

Xue, Changhu1; Chen, Kejie1; Tang, Hui2; Lin, Chaoqi1; Cui, Wenfeng1

2022-08-01

10.1007/s11069-022-05532-3

NATURAL HAZARDS   Impact Factor & Quartile

0921-030X

1573-0840

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.

Earthquake-induced landslides Machine learning Landslide susceptibility analysis

SCI

English

First ; Corresponding

National Natural Science Foundation of China[42074024]

Geology ; Meteorology & Atmospheric Sciences ; Water Resources

Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources

WOS:000837518500001

SPRINGER

GEOSCIENCES

Web of Science

1.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Guangdong, Peoples R China
2.GFZ German Res Ctr Geosci, D-14473 Potsdam, Germany

Xue, Changhu,Chen, Kejie,Tang, Hui,et al. 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[J]. NATURAL HAZARDS,2022.

Xue, Changhu,Chen, Kejie,Tang, Hui,Lin, Chaoqi,&Cui, Wenfeng.(2022).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.NATURAL HAZARDS.

Xue, Changhu,et al."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".NATURAL HAZARDS (2022).