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    Landslide Risk Assessment by Using a New Combination Model Based on a Fuzzy Inference System Method

    69862.pdf (586.7Kb)
    Access Status
    Open access
    Authors
    Azimi, S.
    Nikraz, Hamid
    Yazdani-Chamzini, A.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Azimi, S. and Nikraz, H. and Yazdani-Chamzini, A. 2018. Landslide Risk Assessment by Using a New Combination Model Based on a Fuzzy Inference System Method. KSCE Journal of Civil Engineering. 22 (11): pp. 4263-4271.
    Source Title
    KSCE Journal of Civil Engineering
    DOI
    10.1007/s12205-018-0041-7
    ISSN
    1226-7988
    School
    School of Civil and Mechanical Engineering (CME)
    Remarks

    This is a post-peer-review, pre-copyedit version of an article published in KSCE Journal of Civil Engineering. The final authenticated version is available online at: https://doi.org/10.1007/s12205-018-0041-7

    URI
    http://hdl.handle.net/20.500.11937/69650
    Collection
    • Curtin Research Publications
    Abstract

    Landslides are one of the most dangerous phenomena that pose widespread damage to property and human lives. Over the recent decades, a large number of models have been developed for landslide risk assessment to prevent the natural hazards. These models provide a systematic approach to assess the risk value of a typical landslide. However, often models only utilize the numerical data to formulate a problem of landslide risk assessment and neglect the valuable information provided by experts’ opinion. This leads to an inherent uncertainty in the process of modelling. On the other hand, fuzzy inference systems are among the most powerful techniques in handling the inherent uncertainty. This paper develops a powerful model based on fuzzy inference system that uses both numerical data and subjective information to formulate the landslide risk more reliable and accurate. The results show that the proposed model is capable of assessing the landslide risk index. Likewise, the performance of the proposed model is better in comparison with that of the conventional techniques.

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