During an earthquake sequence, there are often multiple recurring landslides. Understanding the spatial distribution of the landslides triggered by the rst 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 rst 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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.