Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predicting Earthquake-Induced Landslides by Using a Stochastic Modelling Approach Which Combines Preparatory and Triggering Factors: A Case Study of the Coseismic Landslides Occurred on January and February 2001 in El Salvador, CA

Claudio Mercurio
* ORCID logo ,
Laura Paola Calderón-Cucunuba
,
Abel Alexei Argueta-Platero
ORCID logo ,
Grazia Azzara
,
Chiara Cappadonia
ORCID logo ,
Chiara Martinello
ORCID logo ,
Edoardo Rotigliano
ORCID logo ,
Christian Conoscenti
ORCID logo
Version 1 : Received: 13 January 2023 / Approved: 19 January 2023 / Online: 19 January 2023 (11:54:14 CET)

A peer-reviewed article of this Preprint also exists.

Mercurio, C.; Calderón-Cucunuba, L.P.; Argueta-Platero, A.A.; Azzara, G.; Cappadonia, C.; Martinello, C.; Rotigliano, E.; Conoscenti, C. Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides. ISPRS Int. J. Geo-Inf. 2023, 12, 178. Mercurio, C.; Calderón-Cucunuba, L.P.; Argueta-Platero, A.A.; Azzara, G.; Cappadonia, C.; Martinello, C.; Rotigliano, E.; Conoscenti, C. Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides. ISPRS Int. J. Geo-Inf. 2023, 12, 178.

Abstract

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1,259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images taken a few days after the events allowed us to map 6,491 coesismic landslides, which occurred in 14 study areas extending for about 400 km2. Four different Multivariate Adaptive Regression Splines (MARS) models were produced by using different covariate sets and landslide inventories, the latter containing the slope failures triggered by an extreme rainfall event of November 2009 and those induced by the earthquakes of 2001. Moreover, two validation scenarios were employed, including the 25% and 95% of the mapped landslides, respectively. The results of our experiment revealed that: (i) MARS algorithm provides reliable predictions of coesismic landslides; (ii) models calibrated with rainfall-induced landslides predict with acceptable accuracy landslides caused by deep earthquakes and distributed over vast areas; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) a small portion of the landslides produced by an earthquake can be used to calibrate MARS predictive models that help to identify slopes where yet unreported landslides may have occurred.

Keywords

Landslide susceptibility; Multivariate Adaptive Regression Splines (MARS); GIS; earthquake; earthquake-induced landslides; rainfall-induced landslides; El Salvador; Central America

Subject

Environmental and Earth Sciences, Geophysics and Geology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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