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Natural Hazards

, Volume 78, Issue 3, pp 1961–1978 | Cite as

Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier

  • Min-Yuan Cheng
  • Nhat-Duc Hoang
Original Paper

Abstract

This research proposes a novel bee colony optimized support vector classifier (BeeSVC) for predicting typhoon-induced slope collapses. The BeeSVC employs the support vector classifier (SVC) as a machine learning method to classify a slope into two classes: “stable slope” and “collapsed slope.” Furthermore, the artificial bee colony algorithm is used as a metaheuristic to determine the hyper-parameters of the SVC appropriately. The contribution of the proposed method to the body of knowledge is multifold. First, the combined framework of the BeeSVC allows the assessment process to be operated automatically. Second, since the number of the “collapsed” class occupied more than 70 % of the historical cases, a repeated random sub-sampling procedure with the Student’s t test is put forward to alleviate the class-imbalanced problem and reliably evaluate the model performance. Third, the mutual information between the input features and the slope performance is employed to reflect the contribution of each feature to the slope failure. Lastly, the superior performance has proved that the BeeSVC can be a very effective tool for decision-makers to forecast typhoon-induced slope collapses.

Keywords

Support vector classifier Artificial bee colony  Machine learning Typhoon-induced slope collapse 

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Civil and Construction EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Institute of Research and Development, Faculty of Civil EngineeringDuy Tan UniversityDa NangVietnam

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