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Bagging based Support Vector Machines for spatial prediction of landslides

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Abstract

A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging Ensemble and Support Vector Machine (SVM) classifier, was proposed for the spatial prediction of landslides at the district of Mu Cang Chai, Viet Nam. In the present study, 248 past landslides and fifteen geo-environmental factors (curvature, elevation, distance to rivers, slope, aspect, river density, plan curvature, distance to faults, profile curvature, fault density, lithology, distance to roads, rainfall, land use, and road density) were considered for the model construction. Different evaluation criteria were applied to validate the proposed hybrid model such as statistical index-based methods and area under the receiver operating characteristic curve (AUC). The single SVM and the Naïve Bayes Trees (NBT) models were selected for comparison. Based on the AUC values, the proposed hybrid model BSVM (0.812) outperformed the SVM (0.804) and NBT (0.8) models. Thus, the BSVM is a promising and better method for landslide prediction.

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Acknowledgements

We thank the Vietnam Institute of Geosciences and Mineral Resources for sharing the data and the Director, BISAG, DST, GOG, India, for the encouragement and facilities for conducting this research.

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Authors and Affiliations

  1. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

    Binh Thai Pham

  2. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  3. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  4. Department of Science & Technology, Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India

    Indra Prakash

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  1. Binh Thai Pham
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Correspondence to Dieu Tien Bui.

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Pham, B.T., Tien Bui, D. & Prakash, I. Bagging based Support Vector Machines for spatial prediction of landslides. Environ Earth Sci 77, 146 (2018). https://doi.org/10.1007/s12665-018-7268-y

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