基于统计方法耦合地理探测器的地质灾害易发性评价——以吕梁山区为例

目的]以山西省吕梁市吕梁山区的离石、石楼、柳林三区(县)为例研究高精度地质灾害易发性评价模型，为该地区区域规划提供辅助决策支持。方法]基于地理信息系统，以区域内525个历史灾害点及500个非灾害点为样本，选取19个地灾影响因素，应用地理探测器(geographic detectors, GD)判断各因素的相对重要性，在Jupyter Notebook平台展开相关性检验并筛选指标因子，以信息量模型(information method, IM)为基础，利用灾害点计算其所提供的信息量的同时结合非灾害点提供信息量得到指标因子改进信息量模型(improved information method, IIM),并借助地理探测器空间分异性q值计算权重。利用综合确定性系数法(certainty factor, CF)分别建立GD-IIM, GD-IM, GD-CF, IM, CF, IIM共6大评价体系，采用自然断点分类法将研究区易发性依次划分为5,4,3个等级，以种子细胞面积指数(seed cell area index, SCAI)验证其分区结果准确性，采用ROC曲线对比模型结果精确度。结...

Geological Hazard Susceptibility Evaluation Based on a Statistical Method Coupled with Geographic Detector -A Case Study in Mountainous Area of Lüliang City

Objective] A high-precision geological hazard susceptibility evaluation model was determined for the three districts (counties) of Lishi, Shilou, and Liulin in Lüliang City, Shanxi Province in Luliang mountaionous area in order to provide auxiliary decision-making support for regional planning in the area.Methods] Based on a geographic information system, a sample of 525 historical hazard points and 500 randomly selected non-hazard points in the region were used, and 19 influencing factors of geological hazard were selected. Geographic detectors (GD) were used to judge the relative importance of each factor. Correlation tests and filtering index factors were determined on the Jupyter Notebook platform. Based on the information method (IM), a method was proposed to calculate the amount of information provided by disaster points combined with the amount of information provided by non-disaster points to obtain the improved information method (IIM), and to calculate the weight with the help of the spatial heterogeneity q value of geographic detectors. Six evaluation systems (GD-IIM, GD-IM, GD-CF, IM, CF, and IIM) were established using the certainty factor (CF). The natural breakpoint classification method was used to divide the susceptibility into five, four, and three levels, and the accuracy of the partition results was verified by the seed cell area index (SCAI). The accuracy of the model results was compared with the ROC curve.Results] After SCAI testing, the models were divided into four levels (very low, low, high, and very high) that met the rationality requirements. The success rate and prediction rate of disaster susceptibility evaluation by the GD-IIM model reached 90.5 % and 85.5 %, respectively. The IIM model exhibited 2 %~4 % greater accuracy than the traditional IM and CF statistical methods.Conclusion] The bivariate statistical method coupled with geographic detectors produced more accurate results in constructing the vulnerability evaluation prediction model in the study area. Model construction that considered the non-disaster point information was more accurate than the IM model that considered only the disaster point information model. The improved model was suitable for local model construction.