用Logistic回归模型编制滑坡灾害敏感性区划图的方法研究

在GIS环境下应用Logistics回归模型编制地质灾害区划图。针对网格的生成、致灾因子的选择、不重合边界等问题,运用GIS的空间分析功能和SPSS的统计功能,消除了这些问题对滑坡灾害区划图的准确度的影响。结合贵州省的实际,根据历史灾害的分布情况,比较不同方法编制的滑坡灾害敏感性区划图,确定最优编制方法。对基于Logistic回归模型编制滑坡灾害敏感性区划图提出若干指导性建议,丰富和完善了Logistic回归模型编制滑坡灾害区划图方法体系。

基于信息量-机器学习耦合模型的采煤沉陷区滑坡敏感性评价

【目的】采煤沉陷区由于其特殊的地质条件和采煤背景,极易引发地面裂缝和地面塌陷,进而导致滑坡等地质灾害,选择适用的影响因子和训练模型在该地区开展滑坡敏感性评价十分必要。【方法】以北京西山煤矿区为例,采用斜坡单元与数学统计模型和机器学习模型耦合方法,开展采煤沉陷区的滑坡敏感性评价。在地理信息系统环境中,选取坡度、坡向、地表湿润度等地形地貌因子,并加入地质背景的地层岩性和与断层距离因子,采煤背景的与采煤巷道距离和与井口距离等共10个因子作为评价指标,通过相关性分析优化滑坡敏感性评价体系。同时以水文分析法划分的地形斜坡单元为评价基础,分别应用信息量模型(I)信息量-随机森林(I-RF)、信息量-多层感知机(I-MLP)耦合模型对滑坡敏感性进行空间预测。【结果和结论】结果表明,耦合模型(I-RF、I-MLP)的精度均高于独立模型(I),3种模型的AUC值分别为0.861、0.845、0.761,I-RF模型具有更强的预测能力和精度。此外地质和采煤背景因子的加入,优化了滑坡敏感性的评估效果。为了验证滑坡敏感性分区结果的实用性,以北京市“23·7”强降雨事件为时间节点,利用大比例尺航空摄影测量、时序InSAR技术等手段对滑坡敏感性评估结果进行佐证。结果表明,基于斜坡单元和耦合模型的滑坡敏感性评估结果与诱导事件后的滑坡灾害发育情况,有很大程度的吻合性。滑坡敏感性评价结果在一定程度上可以反映各斜坡的滑坡发生概率,对于采煤沉陷区滑坡灾害的预测和防治工作的开展有一定的参考性。

Landslides susceptibility mapping in Guizhou province based on fuzzy theory

The purpose of this study was to assess the susceptibility of landslides around the area of Guizhou province based on fuzzy theory.In first instance, slope, elevation, lithology, proximity to tectonic lines, proximity to drainage and annual precipitation were taken as independent, causal factors in this study.A landslide hazard evaluation factor system was established by classifying these factors into more subclasses according to some rules.Secondly, a trapezoidal fuzzy number weighting(TFNW) approach was used to assess the importance of six causal factors to landslides in an ArcGIS environment.Thirdly, a landslide susceptibility map was created based on a weighted linear combination model.According to this susceptibility map, the study area was classified into four categories of landslide susceptibility:low, moderate, high and very high.Finally, in order to verify the results obtained, the susceptibility map and the landslide inventory map were combined in the GIS.In addition, the weighting procedure showed that TFNW is an efficient method for weighting causal landslide factors.