Artificial neural network ensembles applied to the mapping of landslide susceptibility

This study proposes a comprehensive methodology to the application of an Artificial Neural Network Ensemble (ANNE) for the mapping of landslide susceptibility. The identification of susceptible areas was performed on the basis of landslide inventory databases and seven parameters from three classes: geomorphological (elevation, aspect, slope, topographic moisture index, profile curvature),geological (lithology) and environmental (land use). Studies are presented for two major cities in Brazil, Porto Alegre and Rio de Janeiro. As the main result, we show that the susceptibility maps generated by the ANNE feature higher accuracy than those published by official organs such as the Brazilian Geological Survey and Geotechnical Institute Foundation (Geo-Rio). This indicates that the proposed methodology can be an effective tool to assist the development of reliable landslide susceptibility maps in an efficient and agile manner.