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

  • Binh Thai Pham
  • Abolfazl Jaafari
  • Indra Prakash
  • Dieu Tien Bui
Original Paper


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.


Landslide susceptibility mapping Machine learning Ensembles MultiBoost SVM GIS India 



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


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Binh Thai Pham
    • 1
    • 2
  • Abolfazl Jaafari
    • 3
  • Indra Prakash
    • 4
  • Dieu Tien Bui
    • 5
  1. 1.Geographic Information Science Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityVietnam
  3. 3.Young Researchers and Elite Club, Karaj BranchIslamic Azad UniversityKarajIran
  4. 4.Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG)Government of GujaratGandhinagarIndia
  5. 5.Geographic Information System Group, Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBø i TelemarkNorway

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