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Bagging based Support Vector Machines for spatial prediction of landslides

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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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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  • DOI: https://doi.org/10.1007/s12665-018-7268-y

Keywords

  • Landslides
  • Machine learning
  • Ensemble techniques
  • Bagging
  • Support Vector Machines