期刊
INTERNATIONAL JOURNAL OF DIGITAL EARTH
卷 9, 期 11, 页码 1077-1097出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/17538947.2016.1169561
关键词
Shallow landslide; Least-Squares Support Vector Machines; differential evolution; GIS; Vietnam
资金
- Hanoi University of Mining and Geology, Vietnam [B2014-02-21]
- Geographic Information System group, University College of Southeast Norway
This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DE-LSSVMSLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The model's results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.
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