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Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system-A comparative study

Author

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  • Bipin Peethambaran

    (Indian Institute of Technology Roorkee)

  • R. Anbalagan

    (Indian Institute of Technology Roorkee)

  • K. V. Shihabudheen

    (Indian Institute of Technology Roorkee)

Abstract

A landslide susceptibility map (LSM) is an imperative element in the planning of sustainable development practices and geo-environmental conservations in mountainous terrains. In recent times, approaches that couple soft computing techniques and Geographic Information System (GIS) has emerged as better-suited models that can diminish the flaws and limitations of heuristic, probabilistic and distribution approaches in landslide susceptibility mapping. This paper presents an improved fuzzy expert system (FES) model, a fusion of Mamdani fuzzy inference system (Mamdani-FIS) and frequency ratio method for GIS-based landslide susceptibility mapping. The improved FES model has been applied for mesoscale (1:15,000) landslide susceptibility mapping of Mussoorie Township, Uttarakhand, India, along with conventional fuzzy set procedure (FSP) and an existing FES model, MamLand. The LSMs generated through different procedures have been validated and compared by means of spatial distribution of susceptibility zones and statistical analysis with the help of landslide inventory. The validation and comparative analysis have indicated the significantly better performance of the improved FES model over FSP and MamLand.

Suggested Citation

  • Bipin Peethambaran & R. Anbalagan & K. V. Shihabudheen, 2019. "Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system-A comparative study," 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(1), pages 121-147, March.
    • Handle: RePEc:spr:nathaz:v:96:y:2019:i:1:d:10.1007_s11069-018-3532-4
    DOI: 10.1007/s11069-018-3532-4
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    References listed on IDEAS

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    1. Bakhtiar Feizizadeh & Thomas Blaschke, 2013. "GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, 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. 65(3), pages 2105-2128, February.
    2. Andrea Fabbri & Chang-Jo Chung & Antonio Cendrero & Juan Remondo, 2003. "Is Prediction of Future Landslides Possible with a GIS?," 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 487-503, November.
    3. Kyungjin An & Suyeon Kim & Taebyeong Chae & Daeryong Park, 2018. "Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources," Sustainability, MDPI, vol. 10(2), pages 1-13, January.
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    Cited by:

    1. Luísa Vieira Lucchese & Guilherme Garcia Oliveira & Olavo Correa Pedrollo, 2021. "Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping," 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. 106(3), pages 2381-2405, April.

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