中国是世界上遭受地质灾害最严重的国家之一，地质灾害破坏性强，严重扰乱了人们的生产生活秩序，阻碍了区域经济发展。对地质灾害的形成原因以及分布情况进行调查，掌握一个地区地质灾害风险分布情况就显得十分必要。对地质灾害进行风险性评价，可以为地区防灾减灾工作提供理论依据。

本文以景泰县为研究区域，对县域范围内地质灾害风险进行评价。广泛收集数据，选取评价因子，利用熵权-TOPSIS法对评价因子重要性排序，为层次分析法、模糊层次分析法确定权重做准备，再以信息量法作为对比，对三种评价结果进行验证。基于ArcGIS平台，对景泰县地质灾害进行易发性及易损性评价，进而作出风险性评价。本文主要研究内容及结论如下：

（1）选取坡度、地形起伏度、距断裂带距离、地层岩性、距河流距离、年均降水量、植被覆盖度、距道路距离、距工矿距离这九个因素作为评价因子，与层次分析法、模糊层次分析法、信息量法共同构成地质灾害易发性评价体系。利用统计分析方法验证评价结果，结果表明，模糊层次分析法和信息量法的评价效果相似度高度一致，且明显优于层次分析法。通过差异对比，说明模糊层次分析法得到的权重比层次分析法更加合理。综合考虑，以模糊层次分析法的结果作为最终的地质灾害易发性评价结果。

（2）地质灾害易发性评价结果表明，景泰县地质灾害分布表现出“南多北少”的特点。轻微易发区面积占比27.15％，大部分集中在景泰县中北部地区，小部分分散在景泰县南部，该易发区呈连续面状分布。低易发区面积占比28.84％，主要分布在景泰县中北部，少量分布在南部区域，该易发区呈带状分布，分布轨迹大多与路网重合。中易发区面积占比32.93％，大部分集中在景泰县北部，呈连续面状分布，南部有零星分布。高易发区面积占比11.08％，主要分布在西北、西南及中部地区，东南部有少量分布。

（3）选取人口密度、建筑物密度、路网密度、国内生产总值、夜间灯光共五个指标作为评价因子，与模糊层次分析法共同构成地质灾害易损性评价体系进行易损性评价。选用“平均每个灾害点受威胁人数、平均每个灾害点受威胁财产”两个指标对结果验证。结果表明，这两个指标与易损区呈现正相关的关系，即易损区等级越高，指标对应的值越大。说明评价结果与实际情况吻合度高，可靠性强。

（4）易损性评价结果表明，易损区基本呈现“沿道路—乡镇”分布的特点。评价结果中，轻微易损区面积占比为81.14％，分布范围最广，各乡镇均有分布，普遍存在于山区及远离村落的区域。低易损区面积占比16.18％，在各个乡镇也均有分布，大多呈带状分布，与路网分布吻合度较高，在一条山镇、芦阳镇、草窝滩镇等局部位置呈块状分布。中易损区面积占比2.42％，在各个乡镇也均有分布，除一条山镇及芦阳镇呈大面积块状分布外，其他乡镇均呈零星点状分布。高易损区面积占比0.26％，主要分布在一条山镇，一条山镇承灾体分布密度最大。

（5）以“R=H×V”作为地质灾害风险性评价模型。结果表明，风险区划分基本遵循“易发性程度越高、易损性程度越高，则风险性越高。反之，该地风险性越低”这一原则。评价结果中，低风险区面积占比89.60％，主要集中在除一条山镇之外的其他乡镇。中风险区占比9.74％，在各个乡镇均有分布，在红水镇、漫水滩乡、上沙沃镇呈零星点状分布；在草窝滩镇、一条山镇、芦阳镇呈块状分布，在寺滩乡、喜泉镇、正路镇、中泉镇、五佛乡呈带状分布。高风险区占比0.66％，主要分布于景泰县中部及南部的一条山镇、草窝滩镇、五佛乡、芦阳镇、正路镇、喜泉镇、中泉镇。除在一条山镇呈大面积块状分布外，其他几个乡镇均呈零星点状分布。空间自相关分析结果表明，地质灾害风险性评价结果在空间上呈显著的空间正相关关系，风险区分布在空间上呈现集聚分布的特征。局部空间自相关分析中，高低值分布区与风险性评价中风险区的分布吻合度高，表明风险评价效果较好。

China is one of the countries that suffers the most from geological disasters in the world. Geological disasters are highly destructive, seriously disrupting people's production and living order and hindering regional economic development. It is very necessary to investigate the causes and distribution of geological disasters and to grasp the distribution of geological disasters in a region. Risk assessment of geological disasters can provide theoretical basis for regional disaster prevention and mitigation.

This paper takes Jingtai County as the research area to evaluate the geological disaster risk within the county. Collect data extensively, select evaluation factors, use entropy weight-TOPSIS method to rank the importance of evaluation factors, prepare for AHP and Fuzzy AHP to determine weights, and then use the information method as a comparison to verify the three evaluation results . Based on the ArcGIS platform, the susceptibility and vulnerability assessment of geological disasters in Jingtai County was carried out, and then the risk assessment was made. The main research contents and conclusions of this paper are as follows:

(1) Nine factors, namely slope, topographic relief, distance from fault zone, stratum lithology, distance from river, average annual precipitation, vegetation coverage, distance from road, and distance from industrial and mining industry were selected as evaluation factors, and the analysis of hierarchy process was carried out. method, fuzzy analytic hierarchy process, and information quantity method together constitute a geological disaster susceptibility evaluation system. Statistical analysis method is used to verify the evaluation results. The results show that the evaluation results of the fuzzy analytic hierarchy process and the information quantity method are highly consistent, and are obviously better than the analytic hierarchy process. The difference comparison shows that the weights obtained by fuzzy AHP are more reasonable than those obtained by AHP. Taking into account comprehensively, the result of fuzzy analytic hierarchy process is used as the final evaluation result of geological hazard susceptibility.

(2) The evaluation results of the susceptibility of geological disasters show that the distribution of geological disasters in Jingtai County shows the characteristics of "more in the south and less in the north". The area of mildly prone areas accounted for 27.15%, most of which were concentrated in the central and northern parts of Jingtai County, and a small part was scattered in the southern part of Jingtai County. The prone areas were distributed in a continuous plane. The area of low-risk areas accounted for 28.84%, mainly distributed in the central and northern parts of Jingtai County, and a small amount in the southern area. The area of the middle-prone area accounts for 32.93%, most of which are concentrated in the northern part of Jingtai County, distributed in a continuous plane, and scattered in the southern part. The area of high-risk areas accounted for 11.08%, mainly distributed in the northwest, southwest and central regions, with a small amount in the southeast.

(3) Five indicators including population density, building density, road network density, gross domestic product, and night lights are selected as evaluation factors, and together with the fuzzy analytic hierarchy process, a geological disaster vulnerability evaluation system is formed to evaluate the vulnerability. The results were verified by using two indicators of "the average number of people threatened per disaster site and the average threatened property per disaster site". The results show that the two indicators have a positive correlation with the vulnerable area, that is, the higher the level of the vulnerable area, the greater the corresponding value of the indicators. It shows that the evaluation results are highly consistent with the actual situation and have strong reliability.

(4) Vulnerability evaluation results show that the vulnerable area basically presents the characteristics of "along the road-township" distribution. In the evaluation results, the area of slightly vulnerable areas accounts for 81.14%, and the distribution range is the widest. It is distributed in all towns and towns, and generally exists in mountainous areas and areas far away from villages. The area of low vulnerability area accounts for 16.18%, and it is also distributed in various towns and towns. Most of them are distributed in a band shape, which is in good agreement with the distribution of the road network. block distribution. The area of medium vulnerable area accounts for 2.42%, and it is also distributed in all towns. Except for Yishan Town and Luyang Town, which are distributed in large areas, other towns are distributed in sporadic distribution. The area of high vulnerability area accounted for 0.26%, mainly distributed in a mountain town, and the distribution density of disaster-bearing bodies in a mountain town was the highest.

(5) Take “R=H×V” as the geological hazard risk assessment model. The results show that the division of risk areas basically follows the principle of "the higher the degree of susceptibility and the higher the degree of vulnerability, the higher the risk. On the contrary, the lower the risk is". In the evaluation results, the area of ​​low-risk areas accounted for 89.60%, mainly concentrated in other towns and towns except a mountain town. The medium-risk area accounts for 9.74%, and it is distributed in all towns. It is scattered in Hongshui Town, Manshuitan Township, and Shangshawo Town; It is distributed in a band shape in Sitan Township, Xiquan Town, Zhenglu Town, Zhongquan Town and Wufo Township. High-risk areas accounted for 0.66%, mainly located in Yishan Town, Caowotan Town, Wufo Township, Luyang Town, Zhenglu Town, Xiquan Town, and Zhongquan Town in the central and southern parts of Jingtai County. Except for one mountain town, which is distributed in a large area, several other towns are distributed in sporadic points. The results of spatial autocorrelation analysis show that the geological disaster risk assessment results show a significant positive spatial correlation in space, and the distribution of risk areas presents the characteristics of agglomeration distribution in space. In the local spatial autocorrelation analysis, the distribution of high and low values ​​has a high degree of agreement with the distribution of the risk area in the risk assessment, indicating that the risk assessment effect is good.

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