Evaluation of Loess Landslide Susceptibility Based on Optimised MaxEnt Model: A Case Study of Wuqi County in Shaanxi Province
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摘要:
黄土高原地区滑坡灾害频发，严重危害人民生命财产安全和重大工程建设，进行精准的滑坡易发性评价，识别“什么地方易发生”，有助于高效预测滑坡灾害风险，为防灾减灾提供有效的科学依据。笔者以黄土高原腹地吴起县为例，采用优化最大熵模型（MaxEnt），利用505个滑坡点，选取高程、坡向、坡度、地形粗糙度、岩性、河流缓冲区、降雨、NDWI（地表湿度）及道路缓冲区作为评价因子，并引入InSAR地表形变数据作为动态评价因子，开展了滑坡易发性评价。基于Enmeval数据包调整优化的MaxEnt模型，分别随机选取90%和10%的滑坡点进行模型训练及验证，模型精度高（AUC值为0.855），模拟效果准确可信。引入InSAR地表形变速率作为动态评价因子，模型精度、评价结果均有所提升。评价结果显示：研究区较高易发区面积和高易发区面积分别占吴起县总面积10.27%和6.33%，高、较高易发区内的滑坡点占全部滑坡点的73.27%，滑坡易发性评价结果与滑坡点分布现状吻合，评价效果好。高程、坡度和地表粗糙度对模型模拟结果贡献较高，是研究区滑坡易发性重要评价因子。
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关键词:
- 黄土地貌区 /
- 优化MaxEnt模型 /
- 黄土滑坡 /
- 易发性评价 /
- 吴起县
Abstract:Landslide disasters which occur frequently in the Loess Plateau, seriously endanger the safety of people's lives and property, and affect the construction of major projects. Accurate landslide susceptibility assessment is useful for efficiently and quickly landslide risk prediction, and can provide scientific backing for disaster prevention and reduction by identifying "where landslides are prone". Taking Wuqi County on the Loess Plateau as an example, we use the optimized MaxEnt model and 505 landslide points to evaluate the landslide susceptibility. Elevation, aspect, slope, terrain roughness, lithology, river buffer, rainfall, NDWI (surface humidity), road buffer, and InSAR surface deformation data, which was introduced as dynamic evaluation factors, were selected as influencing factors. The results show: In the MaxEnt model based on Enmeval packet adjustment, when 90% landslide points were randomly selected as the training set and 10% landslide points as the verification set, the model accuracy was the highest (AUC value was 0.855), and the simulation effect was accurate and reliable. InSAR surface deformation rate was introduced as a dynamic evaluation factor, and the model accuracy and evaluation results were both improved. In the study area, the area of high and relatively high susceptibility areas accounted for 10.27% and 6.33% of the total area respectively, and the landslide points in the high and relatively high prone areas accounted for 73.27% of the total landslide points, of which the high prone areas accounted for 48.11%. The evaluation results of landslide susceptibility were consistent with the distribution of landslide points, which proves that the evaluation works well. Elevation, slope and surface roughness contribute significantly to the simulation results, and are important factors affecting the landslide susceptibility.
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Key words:
- loess landform region /
- optimized MaxEnt model /
- loess landslide /
- evaluation /
- Wuqi county
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表 1 基于刀切法评价因子重要性分布表
Table 1. The importance distribution table of evaluation factors based on knife-cutting method
评价因子 贡献率（%） 坡度 38.4 岩性 18.6 高程 18.1 河流缓冲区 9.4 坡向 6.6 道路缓冲区 2.6 NDWI 2.3 地表粗糙度 1.6 地表形变速率 1.2 降雨 1.1 NDBI 0 NDVI 0 平面曲率 0 剖面曲率 0 
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表 2 不同参数设置下MaxEnt模型评价结果
Table 2. Evaluation results of MaxEnt model under different parameters setting
是否引入地表形变速率 模型评价 调控倍频 特征组合 Delta.AICc 10%训练遗漏率 引入地表形变速率 默认 1 LQHPT 13.0896 0.131827 优化 1.5 QHP 0 0.129843 未引入地表形变速率 默认 1 LQHPT 37.4817 0.126679 优化 0.5 QHP 0 0.115884
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表 3 易发性评价结果与滑坡点分布现状对比
Table 3. Comparison of susceptibility evaluation results and landslide point distribution status
易发登记 面积（km2） 面积百分比（%） 滑坡点数量（个） 滑坡点占比 滑坡点密度 高易发区 241.96 6.33 243 48.11 1.01 较高易发区 392.90 10.27 127 25.15 0.32 中易发区 596.17 15.59 80 15.8 0.13 较低易发区 845.52 22.11 44 8.7 0.05 低易发区 1747.84 45.70 11 2.2 0.1 合计 3824.39 100% 505 100% /
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表 4 各评价因子的贡献率和置换重要性比值表
Table 4. Contribution and inportance of impact variables affecting the landslide susceptibility
评价因子 因子贡献率（%） 置换重要性（%） 高程 25.2 33.9 坡度 20.1 24.2 粗糙度 14 11.3 河流缓冲区 11.7 7.3 岩性 11.3 4.1 降雨 7.1 7.9 坡向 5.1 2.2 地表变形速率 3.2 3.6 道路缓冲区 1.5 4.3 NDWI 0.7 1.1
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