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摘要:
勉略宁地处秦巴山区，是陕西省地质灾害发育最严重的地区之一。笔者基于GIS与机器学习技术，采用了与地质灾害发生密切相关的12种因子，通过构建样本集，选用5种机器学习算法进行勉略宁地区的地质灾害危险性建模。实验结果标明，随机森林模型能够更好地模拟勉略宁地区的地质灾害发生情况。通过该模型对勉略宁地区进行地质灾害危险区进一步划分，从而指导地质灾害调查与防治工作。
Abstract:Mianluening, located in Qinba mountain area, is one of the areas with the most serious geohazards in Shaanxi Province. This paper adopts 12 factors closely related to the occurrence of geohazards to construct a sample set based on GIS and machine learning technology, and then selects five machine learning algorithms to model the risk of geohazards in Mianluening area. The experiments demonstrated that the Random Forests model is the most suitable one to simulate the occurance of geohazards in the study area. Further division of geohazards risk is then performed by the aforementioned model, which is useful to guide the geohazards investigation and prevention.
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Key words:
- geohazards /
- risk evaluation /
- Mianluening area /
- Machine learning
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表 1 评价因子表
Table 1. Evaluation Factors
一级因子 二级因子 三级因子 获取方式 孕灾
因子地形地貌 高程 DEM数据处理 坡度 DEM数据处理 坡向 DEM数据处理 曲率 DEM数据处理 地质条件 地质工程分区 原始资料矢量化 距断裂带距离 距离分析 气候条件 降雨量 多年年平均
降雨量水文条件 距河流距离 距离分析 环境条件 归一化植被指数（NDVI） 遥感数据处理 诱发
因子人类工程
活动土地利用类型 栅格图 距离农村
居民点距离距离分析 距路网距离 距离分析 
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表 2 模型测试结果对比表
Table 2. Comparison of model test results
RF模型 预测值 准确率 0.95 1 0 精确度 0.96 真实值 1 216 11 召回率 0.95 0 10 221 F1值 0.95 LR模型 预测值 准确率 0.83 1 0 精确度 0.82 真实值 1 193 34 召回率 0.85 0 42 189 F1值 0.84 SVM模型 预测值 准确率 0.78 1 0 精确度 0.73 真实值 1 198 29 召回率 0.87 0 74 157 F1值 0.79 NB模型 预测值 准确率 0.81 1 0 精确度 0.75 真实值 1 205 22 召回率 0.90 0 67 164 F1值 0.82 GBDT模型 预测值 准确率 0.95 1 0 精确度 0.94 真实值 1 215 12 召回率 0.95 0 13 218 F1值 0.95
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