Slope stability analysis: a support vector machine approach
 Pijush Samui
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Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%.
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 Title
 Slope stability analysis: a support vector machine approach
 Journal

Environmental Geology
Volume 56, Issue 2 , pp 255267
 Cover Date
 20081101
 DOI
 10.1007/s0025400711614
 Print ISSN
 09430105
 Online ISSN
 14320495
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Artificial Neural Network
 Slope stability
 Support Vector Machine
 Industry Sectors
 Authors

 Pijush Samui ^{(1)}
 Author Affiliations

 1. Department of Civil Engineering, Indian Institute of Science, Bangalore, 560 012, India