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Application of a semiquantitative and GIS-based statistical model to landslide susceptibility zonation in Kayangan Catchment, Java, Indonesia

Author

Listed:
  • Danang Sri Hadmoko

    (Universitas Gadjah Mada)

  • Franck Lavigne

    (University Paris 1 Panthéon Sorbonne)

  • Guruh Samodra

    (Universitas Gadjah Mada)

Abstract

Kayangan Catchment, one of the extremely landslide susceptible areas in Indonesia, is situated on the eastern flank of Menoreh Mountain in Yogyakarta Province on the island of Java. Landslides cause land and infrastructure damages because of their frequency in human settlements. The objectives of this study are twofold: (1) to analyze the spatial distribution of landslides and its correlation using terrain parameters; and (2) to analyze landslide susceptibility using both semiquantitative and statistical methods, i.e., analytical hierarchy process (AHP) and information value (IV) methods. Nine parameter maps were introduced to assess landslide susceptibility. The parameter maps and landslide distribution map were spatially overlaid to calculate the contribution of each parameter to landslide susceptibility. The landslide susceptibility map encompassed four different categories: very high, high, medium, and low susceptibility. The map was validated through a success rate curve by determining the area under the curve using existing landslide events. The success rate curves indicated that the IV was more accurate than the AHP, although both of them had high correlations. Both methods show that the precondition factors represented approximately 80% of the influence on landslide occurrence, with the remaining 20% attributed to the triggering factors, primarily rainfall and seismic factors.

Suggested Citation

  • Danang Sri Hadmoko & Franck Lavigne & Guruh Samodra, 2017. "Application of a semiquantitative and GIS-based statistical model to landslide susceptibility zonation in Kayangan Catchment, Java, Indonesia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 437-468, May.
  • Handle: RePEc:spr:nathaz:v:87:y:2017:i:1:d:10.1007_s11069-017-2772-z
    DOI: 10.1007/s11069-017-2772-z
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    References listed on IDEAS

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    1. Danang Hadmoko & Franck Lavigne & Junun Sartohadi & Pramono Hadi & Winaryo, 2010. "Landslide hazard and risk assessment and their application in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(3), pages 623-642, September.
    2. Hamid Pourghasemi & Biswajeet Pradhan & Candan Gokceoglu, 2012. "Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 965-996, September.
    3. Ali Yalcin & Fikri Bulut, 2007. "Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 41(1), pages 201-226, April.
    4. C. van Westen & N. Rengers & R. Soeters, 2003. "Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 399-419, November.
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    Cited by:

    1. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 249-274, August.
    2. Cahio Guimarães Seabra Eiras & Juliana Ribeiro Gonçalves de Souza & Renata Delicio Andrade de Freitas & César Falcão Barella & Tiago Martins Pereira, 2021. "Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1427-1442, June.
    3. Amit Bera & Bhabani Prasad Mukhopadhyay & Debasish Das, 2019. "Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 96(2), pages 935-959, March.

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