Predicting Landslide Deposit Zones: Insights from Advanced Sampling Strategies in the Ilopango Caldera, El Salvador

Laura Paola Calderon-Cucunuba, Abel Alexei Argueta-Platero, Tomás Fernández, Claudio Mercurio, Chiara Martinello, Edoardo Rotigliano and Christian Conoscenti ()
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Laura Paola Calderon-Cucunuba: Dipartimento di Scienze della Terra e del Mare (DiSTeM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Abel Alexei Argueta-Platero: Dipartimento di Scienze della Terra e del Mare (DiSTeM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Tomás Fernández: Department of Cartographic, Geodetic and Photogrammetric Engineering, Centre for Advanced Studies in Earth Sciences, Energy and Environment. University of Jaén, 23071 Jaén, Spain
Claudio Mercurio: Dipartimento di Scienze della Terra e del Mare (DiSTeM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Chiara Martinello: Dipartimento di Scienze della Terra e del Mare (DiSTeM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Edoardo Rotigliano: Dipartimento di Scienze della Terra e del Mare (DiSTeM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Christian Conoscenti: Dipartimento di Scienze della Terra e del Mare (DiSTeM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy

Land, 2025, vol. 14, issue 2, 1-28

Abstract: In landslide susceptibility modeling, research has predominantly focused on predicting landslides by identifying predisposing factors, often using inventories primarily based on the highest points of landslide crowns. However, a significant challenge arises when the transported mass impacts human activities directly, typically occurring in the deposition areas of these phenomena. Therefore, identifying the terrain characteristics that facilitate the transport and deposition of displaced material in affected areas is equally crucial. This study aimed to evaluate the predictive capability of identifying where displaced material might be deposited by using different inventories of specific parts of a landslide, including the source area, intermediate area, and deposition area. A sample segmentation was conducted that included inventories of these distinct parts of the landslide in the hydrographic basin of Lake Ilopango, which experienced debris flows and debris floods triggered by heavy rainfall from Hurricane Ida in November 2009. Given the extensive variables extracted for this evaluation (20 variables), the Induced Smoothed (IS) version of the Least Absolute Shrinkage and Selection Operator (LASSO) methodology was employed to determine the significance of each variable within the datasets. Additionally, the Multivariate Adaptive Regression Splines (MARS) algorithm was used for modeling. Our findings revealed that models developed using the deposition area dataset were more effective compared with those based on the source area dataset. Furthermore, the accuracy of models using deposition area data surpassed that of that using data from both the source and intermediate areas.

Keywords: sampling strategies; deposit areas; debris flows; debris floods; variable significance; landslide inventory (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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