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This study investigates landslide susceptibility mapping in Wayanad district, Kerala (India), utilizing a hybrid approach that combines the Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) methods to assign weights to various influencing parameters. The research applied three novel Multi-Criteria Decision-Making (MCDM) models: Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Combined Compromise Solution (CoCoSo), and Grey Relational Analysis (GRA), alongside traditional methods to generate comprehensive landslide susceptibility zonation maps. The most susceptible areas identified include Periya, Thondernad, and Vellamunda in the north-west, and Chooralmala, Mundakkai, and Vellarimala in the south-west, which experienced significant landslides on July 30, 2024. Among the models, AHP–CoCoSo (AUC = 0.876, TPR = 0.854) and FR–CoCoSo (AUC = 0.899, TPR = 0.854) exhibited the highest predictive accuracy. Overall, 18.43% of the study area falls within the ‘very high’ susceptibility zone, while 17.31% is classified as high susceptibility. The integration of AHP and FR analyses with these advanced MCDM models provides a robust framework for effective landslide risk management, facilitating targeted disaster mitigation strategies in the region.

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Application of MARCOS, CoCoSo, and GRA MCDM Models in a Hybrid Approach for Landslide Susceptibility Assessment in Wayanad District, Kerala (India)

  • 章节

  • 发表日期：

    2025年

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  • Rajkumar Guria,
  • Manoranjan Mishra,
  • Jayanta Das

摘要

This study investigates landslide susceptibility mapping in Wayanad district, Kerala (India), utilizing a hybrid approach that combines the Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) methods to assign weights to various influencing parameters. The research applied three novel Multi-Criteria Decision-Making (MCDM) models: Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Combined Compromise Solution (CoCoSo), and Grey Relational Analysis (GRA), alongside traditional methods to generate comprehensive landslide susceptibility zonation maps. The most susceptible areas identified include Periya, Thondernad, and Vellamunda in the north-west, and Chooralmala, Mundakkai, and Vellarimala in the south-west, which experienced significant landslides on July 30, 2024. Among the models, AHP–CoCoSo (AUC = 0.876, TPR = 0.854) and FR–CoCoSo (AUC = 0.899, TPR = 0.854) exhibited the highest predictive accuracy. Overall, 18.43% of the study area falls within the ‘very high’ susceptibility zone, while 17.31% is classified as high susceptibility. The integration of AHP and FR analyses with these advanced MCDM models provides a robust framework for effective landslide risk management, facilitating targeted disaster mitigation strategies in the region.