0000000000675039
AUTHOR
Laura Paola Calderon-cucunuba
Stochastic assessment of landslide susceptibility by using five different instability datasets: a case study from the southern sector of the “Via al Llano” highway (Colombia)
In this study, the ability of stochastic models to predict landslide susceptibility in the southern sector of the “Via al Llano” highway (Colombia) was assessed. To this aim, an inventory of landslides occurred in the area was prepared by analyzing images available in Google Earth. Multivariate Adaptive Regression Splines (MARS) was employed to model the spatial distribution of the following five data sets of unstable cells selected within each landslide: i) the highest cell (data set MAX), ii) the highest 10% of cells (data set SUP), iii) the lowest cell (data set MIN), iv) the lowest 10% of cells (data set INF), and v) the entire landslide area (data set BODY). The goal of our experiment …
Predicting depositional areas of landslide susceptibility comparing four datasets extracted from landslide area: a case of study after rainfall-induced landslides by Ida Hurricane in 2009 on Ilopango Lake, El Salvador.
Hurricane Ida and low-pressure system 96E crossed Central American countries in 2009. However, in El Salvador, the torrential rainfalls caused many flooding and landslides. As a result, over 200 causalities and the destruction of several villages, and bridges occurred along the mountain slopes. The remote analysis allowed us to prepare an inventory of landslides that occurred after the Hurricane in a basin located in the northern part of Ilopango Caldera. Five groups of data sets were created using selected pixels of each landslide area in order to evaluate the capacity to predict the lowest and the entire landslide area. Multivariate Adaptive Regression Splines (MARS) were employed to mode…