1 research outputs found
Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides
In January and February 2001, El Salvador was hit by two strong earthquakes that triggered
thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and
SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and
flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive
regression splines (MARS) models were produced using different predictors and landslide inventories
which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the
earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and
the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold
spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides
reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was
observed when including susceptibility to rainfall-triggered landslides as an independent variable;
(iii) the best accuracy is achieved by models trained with both preparatory and trigger variables;
(iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be
used to identify potential locations of yet unreported landslides