Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy
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Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy Cristiano Ballabio · Simone Sterlacchini
Received: 14 December 2009 / Accepted: 29 November 2011 / Published online: 3 January 2012 © International Association for Mathematical Geosciences 2011
Abstract The aim of this study is the application of support vector machines (SVM) to landslide susceptibility mapping. SVM are a set of machine learning methods in which model capacity matches data complexity. The research is based on a conceptual framework targeted to apply and test all the procedural steps for landslide susceptibility modeling from model selection, to investigation of predictive variables, from empirical cross-validation of results, to analysis of predicted patterns. SVM were successfully applied and the final susceptibility map was interpreted via success and prediction rate curves and receiver operating characteristic (ROC) curves, to support the modeling results and assess the robustness of the model. SVM appeared to be very specific learners, able to discriminate between the informative input and random noise. About 78% of occurrences was identified within the 20% of the most susceptible study area for the cross-validation set. Then the final susceptibility map was compared with other maps, addressed by different statistical approaches, commonly used in susceptibility mapping, such as logistic regression, linear discriminant analysis, and naive Bayes classifier. The SVM procedure was found feasible and able to outperform other techniques in terms of accuracy and generalization capacity. The over-performance of SVM against the other techniques was around 18% for the cross-validation set, considering the 20% of the most susceptible area. Moreover, by analyzing receiver operating characteristic (ROC) curves, SVM appeared to be less prone to false positives than the other models. The study was applied in the Staffora river basin (Lombardy, Northern Italy), an area of about 275 km2 characterized by a C. Ballabio () Environmental and Land Sciences Dept., Università degli Studi di Milano-Bicocca, Milano 20126, Italy e-mail: [email protected] S. Sterlacchini Institute for the Dynamic of Environmental Processes, National Research Council (CNR-IDPA), Milano 20126, Italy
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Math Geosci (2012) 44:47–70
very high density of landslides, mainly superficial slope failures triggered by intense rainfall events. Keywords Support Vector Machines · Landslide susceptibility mapping · Spatial prediction · Cross-validation
1 Introduction The main aim of landslide susceptibility modeling is to identify areas prone to future mass movements, on the basis of previous knowledge about the spatial distribution of past occurrences. The basic idea behind this approach is to identify areas where a particular combination of physical properties could indicate the predisposition towards similar events in the future. This goal is usually achieved by relating the spatial distribution of past landslides with the spatial
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