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Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

  • Mahvash Gholami
  • Esmaeil Nekouei Ghachkanlu
  • Khabat Khosravi
  • Saied PirastehEmail author
Article
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Abstract

This study presented the prediction capability of three methods including the frequency ratio (FR), fuzzy gamma (FG) and landslide index method (LIM) to produce landslide-prone areas in the Sari-Kiasar watershed, Mazandaran Province of Iran. In the first step, 105 landslide locations were selected and were randomly divided into two groups of 75% (78 locations) and 25% (27 locations) as training and testing datasets. Then the 17 landslide conditioning factors including land use/land cover, Differential Vegetation Index (DVI), lithology and distance from faults, elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature, distance from drainage, rainfall, Stream Power Index, Sediment Transport Index and temperature, and distance from road, density of settlement were considered for the proposed modelling approach. Finally, by applying the training dataset, three landslide susceptibility maps were constructed by using the FR, FG and LIM methods. The prediction capability of the performed model was evaluated by the area under the receiver operating curve or AUC for both training (success rate) and testing (prediction rate) datasets. The results showed that the AUC for success rate of FR, FG and LIM models was 82.04%, 81.08% and 73.61% and for prediction rate was 82.72%, 79.09% and 65.45%, respectively. The results showed that the FR model has a higher prediction accuracy than the FG and LIM methods. This study revealed that the most important factors in landslide occurrence are rainfall, slope and vegetation. The result of the present study can be possibly useful for land use planning and watershed management.

Keywords

Landslide susceptibility FR fuzzy gamma LIM Sari-Kiasar 

Notes

Acknowledgements

The authors are thankful to the Mazandaran Regional Water Authority and the Natural Resources Bureau of Mazandaran for providing data to accomplish this study.

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Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  • Mahvash Gholami
    • 1
  • Esmaeil Nekouei Ghachkanlu
    • 2
  • Khabat Khosravi
    • 3
  • Saied Pirasteh
    • 4
    Email author
  1. 1.Department of Watershed Management Engineering, College of Natural Resources and Marine SciencesTarbiat Modares UniversityNoorIran
  2. 2.Department of Earth Sciences, College of SciencesShiraz UniversityShirazIran
  3. 3.Department of Watershed Management EngineeringSari Agricultural Science and Natural Resources UniversitySariIran
  4. 4.Department of Surveying and GeoinformaticsFaculty of Geosciences and Environmental Engineering (FGEE), Southwest Jiaotong UniversityChengduChina

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