Research on landslide susceptibility based on Mamdani-FIS model

ZHANG Ren-lan; WANG Shao-jun; LI Jiang-feng

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Research on landslide susceptibility based on Mamdani-FIS model

更新时间：2026-06-25

- Research on landslide susceptibility based on Mamdani-FIS model
- Vol. 35, Issue S2, (2014)
- 作者机构：
  
  中国地质大学（武汉）公共管理学院,武汉,430074
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP79
- Online First：12 November 2014，
  
  Published：2014
- 稿件说明：
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ZHANG Ren-lan, WANG Shao-jun, LI Jiang-feng. Research on landslide susceptibility based on Mamdani-FIS model[J]. 2014, 35(S2).
DOI：

ZHANG Ren-lan, WANG Shao-jun, LI Jiang-feng. Research on landslide susceptibility based on Mamdani-FIS model[J]. 2014, 35(S2). DOI：

摘要

滑坡的形成是众多非线性关系的影响因子相互作用的结果，传统滑坡预测方法需要大量实地勘查数据。利用Mamdani FIS（模糊推理系统）模型对三峡库区巴东-秭归段进行滑坡易发性预测，并对结果进行评价。通过地理信息系统（geographic information system

GIS）、遥感（remote sensing，RS）技术和区域地质背景资料获取地形类、生态环境类和地质背景类共3类7种滑坡影响因子，建立了192条相关的推理规则，在Matlab平台下基于Mamdani FIS模型得到研究区滑坡易发性预测指数，并生成滑坡易发性区划图。预测结果的受试者工作特征曲线下的面积值为82.8%，显示滑坡评估效果良好。结果证明，与其他模型相比，基于空间信息技术的Mamdani FIS模型，利用其非线性分析能力和基于专家意见的推理规则，评估滑坡易发性时不需要先验知识支撑，简化了模型使用时对数据的要求。另外，该模型只需通过专家意见改变推理规则就可以应用于不同的地质地理环境区域，显示其较强的适应性。

Abstract

Landslide is the result of the interaction among several nonlinear factors; and a large amount of field data is needed to predict it. The Mamdani fuzzy inference system(FIS) model is applied to the Badong-Zigui area in Three Gorges Reservoir Region; and the results are analyzed. Based on the 3 kinds of factors (geomorphology

eco-environment and geology)

which are collected by GIS and RS

192 rules of inference are established. The regional landslide prone zoning is mapped and the susceptibility indices are derived using the Mamdani FIS

which is developed and employed in Matlab. The area under curve (AUC) obtained from the receiver operating characteristics (ROC) based on results is 82.8%

indicating that this landslide susceptibility map is successful. With the parameters selected by spatial information technology

the Mamdani FIS using its nonlinear analysis capabilities

based on expert opinion and specific inference rules

is free from prior knowledge

simplifying the data requirement. In addition

the model could suit to different areas with simply changes in reference rules based on expert opinion

demonstrating a strong adaptability.

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