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Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment |
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| Authors: | María José Domínguez-Cuesta Montserrat Jiménez-Sánchez Ana Colubi Gil González-Rodríguez |
| Institution: | 1. Departamento de Geología, Universidad de Oviedo, 33005, Oviedo, Spain 2. Departamento de Estadística e I.O. y D.M., Universidad de Oviedo, 33007, Oviedo, Spain 3. European Centre for Soft Computing, 33600, Mieres, Spain |
| Abstract: | A new method for estimating shallow landslide susceptibility by combining Geographical Information System (GIS), nonparametric kernel density estimation and logistic regression is described. Specifically, a logistic regression is applied to predict the spatial distribution by estimating the probability of occurrence of a landslide in a 16 km2 area. For this purpose, a GIS is employed to gather the relevant sample information connected with the landslides. The advantages of pre-processing the explanatory variables by nonparametric density estimation (for continuous variables) and a reclassification (for categorical/discrete ones) are discussed. The pre-processing leads to new explanatory variables, namely, some functions which measure the favourability of occurrence of a landslide. The resulting model correctly classifies 98.55% of the inventaried landslides and 89.80% of the landscape surface without instabilities. New data about recent shallow landslides were collected in order to validate the model, and 92.20% of them are also correctly classified. The results support the methodology and the extrapolation of the model to the whole study area (278 km2) in order to obtain susceptibility maps. |
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