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Earth Science Informatics

, Volume 12, Issue 4, pp 615–628 | Cite as

Mapping landslide susceptibility in the Zagros Mountains, Iran: a comparative study of different data mining models

  • Mohammad Fallah-Zazuli
  • Alireza VafaeinejadEmail author
  • Ali Asghar Alesheykh
  • Mahdi Modiri
  • Hossein Aghamohammadi
Research Paper
  • 84 Downloads

Abstract

In recent years, increasing efforts have been made to predict the time, location, and magnitude of future landslides. This study explores the potential application of four state-of-the-art data mining models (logistic regression, random forest, support vector machine, and Naïve Bayes tree) for the spatially explicit prediction of landslide susceptibility across a landslide-prone landscape in the Zagros Mountains, Iran. Fifteen conditioning factors and 272 historical landslide events were used to develop a geospatial database for the study area. A two-step factor analysis procedure based on the multicollinearity analysis and the Gain Ratio technique was performed to measure the predictive utility of the factors and to quantify their contribution to landslide occurrences across the study region. Once the models were successfully trained and validated using several performance metrics (i.e., ROC-AUC, sensitivity, specificity, accuracy, RMSE, and Kappa), they were applied to the entire study region to generate distribution maps of landslide susceptibilities. Overall, the random forest model demonstrated the highest training performance (AUC = 0.971; accuracy = 99%; RMSE = 0.120) and ability to predict future landslides (AUC = 0.942; accuracy =87%; RMSE = 0.312), followed by the support vector machine, Naïve Bayes tree, and logistic regression models. The Wilcoxon signed-rank test further proved the superiority of the random forest model for mapping landslide susceptibility in the Zagros region. The insights obtained from this research could be useful for the spatially explicit assessment of landslide-prone landscapes and obtaining a better understanding of the capability of different predictive models.

Keywords

Landslide Susceptibility mapping GIS Data mining 

Notes

Acknowledgements

This study was supported by the Science and Research Branch Sensing Islamic Azad University. The authors would like to thank the administrative office of natural resources of the Chaharmahal and Bakhtiari Province, Iran, which provided the landslide report database.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mohammad Fallah-Zazuli
    • 1
  • Alireza Vafaeinejad
    • 2
    Email author
  • Ali Asghar Alesheykh
    • 3
  • Mahdi Modiri
    • 4
  • Hossein Aghamohammadi
    • 1
  1. 1.Department of GIS and RS, Faculty of Natural Resources and EnvironmentScience and Research Branch, Islamic Azad UniversityTehranIran
  2. 2.Faculty of Civil, Water, and Environmental EngineeringShahid Beheshti UniversityTehranIran
  3. 3.Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics EngineeringK. N. Toosi University of TechnologyTehranIran
  4. 4.Department of Urban PlanningMalek-e-Ashtar University of TechnologyTehranIran

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