Estimation of Susceptibility to Landslides Using Neural Networks Based on the FALCON-ART Model

Abstract

Landslides are processes of erosion of catastrophic character which alter the morphology of the landscape and affect people, productive land and infrastructure. Recently, there have been several attempts to apply neural networks to predict landscape susceptibility to landslides. However, the knowledge of the neural network is expressed in a mathematical model that does not allow establishing, intuitively, relationships between the factors causing landslides. This makes it difficult for experts to interpret the output of the network, to support their results with a set of inference rules. This limitation could be overcome by a model based on the FALCON neural network, which allows not only a classification for data clustering with fuzzy logic, but also generates a set of fuzzy rules from data training. For this reason, the FALCON-ART neural network has been implemented in this study to create a set of models of susceptibility to landslides on the watershed of the Caramacate River in north-central. The input data of the model included a landslide scar map from 1992, and variables derived from a digital elevation model and a SPOT-satellite image. A cross validation determined that the best result achieved a 74% success rate in predicting areas susceptible to landslides.