A Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides
Publication: Journal of Computing in Civil Engineering
Volume 30, Issue 5
Abstract
In mountainous regions, landslides are the typical disasters that have brought about significant losses of human life and property. Therefore, the capability of making accurate landslide assessments is very useful for government agencies to develop land-use planning and mitigation measures. The research objective of this paper is to investigate a novel methodology for spatial prediction of landslides on the basis of the relevance vector machine classifier (RVMC) and the cuckoo search optimization (CSO). The RVMC is used to generalize the classification boundary that separates the input vectors of landslide conditioning factors into two classes: landslide and nonlandslide. Furthermore, the new approach employs the CSO to fine-tune the basis function’s width used in the RVMC. A geographic information system (GIS) database has been established to construct the prediction model. Experimental results point out that the new method is a promising alternative for spatial prediction of landslides.
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Published In
Journal of Computing in Civil Engineering
Volume 30 • Issue 5 • September 2016
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© 2016 American Society of Civil Engineers.
History
Received: Mar 17, 2015
Accepted: Oct 2, 2015
Published online: Jan 4, 2016
Discussion open until: Jun 4, 2016
Published in print: Sep 1, 2016
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