Artificial Neural Networks and Robustness Analysis in Landslide Susceptibility Zonation

This contribution focuses on the application of artificial neural networks (ANNs) in landslide susceptibility zonation taking into consideration both the prediction capability assessment and the sensitivity to measurement errors in the obtained models. We suggest a general procedure to perform susceptibility analysis by means of ANNS, introducing robustness analysis as the final step in the susceptibility modelling in order to test the reliability of the obtained maps with respect to errors in measuring, and calculating, the conditioning factors. Such robustness analysis has been performed by calculating a robustness index both for each conditioning factors and for the total model; this allowed us to find the errors in the conditioning factors which affect the neural computation in a greater way and the overall robustness of the model. The experimental results, obtained on the Deba Valley database, suggest that ANNs are a proper method to analyze a complex relationship between conditioning factors and landslides, and that the robustness analysis is a crucial step in the susceptibility modeling, specially as an iterative procedure for variables selection.