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A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

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Abstract

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.

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Acknowledgements

We thank to the Director of BISAG, Government of Gujarat, Gandhinagar, Gujarat, India, for helping and supporting this study.

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Authors and Affiliations

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

    Binh Thai Pham

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

    Binh Thai Pham

  3. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran

    Abolfazl Jaafari

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

    Indra Prakash

  5. Geographic Information System Group, Department of Business Administration and Computer Science, University College of Southeast Norway, Hallvard Eikas Plass, N-3800, Bø i Telemark, Norway

    Dieu Tien Bui

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  1. Binh Thai Pham
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  4. Dieu Tien Bui
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Pham, B.T., Jaafari, A., Prakash, I. et al. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull Eng Geol Environ 78, 2865–2886 (2019). https://doi.org/10.1007/s10064-018-1281-y

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