Landslide Susceptibility Assessment Based on GIS and MaxEnt Model: Example from Central Districts in Tongchuan City
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摘要:
铜川市属于中国资源枯竭型城市，近年来过量的开采资源与频繁的工程活动诱发了大量的滑坡，对人民安全与社会发展造成了严重威胁，如何科学合理地对滑坡易发性进行评价具有重大的研究意义。以铜川市滑坡分布较多的王益区、印台区作为研究区，选取坡度、坡向、高程、曲率、距道路的距离、距水系的距离、地形地貌、岩土体类型等8个因子构建评价指标体系，采用MaxEnt模型与ArcGIS平台相结合的方法构建了研究区滑坡易发性评价模型，并进行了易发性评价。评价结果显示，MaxEnt模型AUC值达到0.905，评价能力优秀；Kappa系数为0.76，评价结果与滑坡现状分布十分吻合；距水系的距离、地形地貌为最重要的环境影响因子。高易发和较高易发主要分布在其中部及东部居民集中居住区，分别占研究区总面积的4.36%、5.77%，与实地调查结果相符，MaxEnt模型可在类似区域滑坡易发性评价中进行推广。
Abstract:Tongchuan city is a resource-depleted city in China. In recent years, excessive resource exploitation and frequent engineering activities have induced a large number of landslides, which threaten people’s safety and social development, and it is of great research significance to evaluate landslide susceptibility in a scientific and reasonable way. The study area is Wangyi and Yintai, which are the two districts with more landslide distribution in Tongchuan City, and the evaluation index system is constructed by selecting eight factors, including slope, slope direction, elevation, curvature, distance from road, distance from water system, geomorphology type and geotechnical type, etc. The model and platform are combined to construct the evaluation model of landslide susceptibility in the study area, and the susceptibility evaluation is carried out. The evaluation results show that the model value reaches 0.905, with excellent evaluation ability, and the coefficient is 0.76. The evaluation results are in good agreement with the current distribution of landslides; the distance from water system and geomorphological type are the most important environmental impact factors. The high susceptibility and high susceptibility account for 4.36% and 5.77% of the total area, mainly in the central and eastern residential areas of the study area, which is consistent with the field survey results.
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Key words:
- susceptibility assessment /
- MaxEnt model /
- ArcGIS /
- landslide
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表 1 AUC值与Kappa值评价标准表
Table 1. Assessment standard of AUC value and kappa value
精确度 极差 较差 一般 较好 优秀 AUC 0.5～0.6 0.6～0.7 0.7～0.8 0.8～0.9 0.9～1 Kappa 0～0.2 0.2～0.4 0.4～0.55 0.55～0.7 0.7～1 
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表 2 AUC均值/SD值与训练比例的关系表
Table 2. Relationship between AUC mean value/SD value and training proportion
训练样本比例 70% 75% 80% 85% 90% AUC平均值 0.902 0.905 0.909 0.904 0.887 标准差 0.0763 0.0661 0.0855 0.0839 0.0565
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