期刊
GEOMATICS NATURAL HAZARDS & RISK
卷 4, 期 1, 页码 1-12出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/19475705.2012.684725
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This article examines the potential of support vector machine (SVM) in stability status prediction of slope. Support vector machine achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model rather than the minimizing the error on the training data only. This study uses SVM as a classification tool. In this article, the input data for slope stability prediction consist of values of geotechnical and geometric properties of slope. The accuracy of the SVM is 100% for this problem. The developed SVM gives also an equation for prediction of status of slope. This study shows that SVM has the potential to be a useful and practical tool for prediction of slope stability.
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