随着全球气候的不断变化以及城镇化进程的不断加剧，城市极端降雨事件逐渐增多，洪涝灾害已经成为影响社会经济发展、人民群众生命和财产安全的主要自然灾害之一。在灾害研究领域，灾害风险评估是应对灾害极其重要的非工程性举措，其评估结果可为有关部门制定防灾减灾方案提供科学依据，也可为应急管理部门快速识别风险的空间分布及做出正确决策提供重要帮助。本文以陕西省为研究区，基于遥感数据、格网化的地理背景数据、空间化的社会经济数据等，利用ArcGIS的空间数据处理、空间分析及模型构建等功能，开展洪涝灾害风险评估及驱动力研究，研究成果如下：

（1）洪涝灾害影响因子驱动力分析。根据陕西省洪涝灾害发生情况，统计洪涝灾害历史调查结果，最终选取10类驱动因子，定量分析各驱动因子对洪涝灾害的影响程度，利用地理探测器模型对洪涝灾害因子进行驱动力分析，研究发现，降水是导致洪涝灾害发生的直接原因，坡度是导致洪涝灾害发生的基础条件，人类的不合理活动加剧了洪涝灾害的发生，多因子共同作用会提高其发生频率。

（2）构建洪涝灾害风险指标评价体系。根据陕西省实际概况，基于以往学者研究成果，结合驱动力分析结果及数据的全面性和可获取性等原则，选择以洪涝灾害暴露性、危险性、脆弱性和防灾减灾能力等构建陕西省洪涝灾害评估的一级指标，选择人口密度、地区生产总值、坡度、河流密度、降水量、耕地面积、老少人口数量、防御洪水能力、避难场所密度、监测能力、医疗救助能力和居民自救能力共12个评价指标为二级指标，建立风险指标评价体系。

（3）风险指标权重的确定。基于系统层级结构的建立、构造判断矩阵、层次单排序和判断矩阵的一致性检验四个部分的层次分析法（AHP）建立层次分析模型，利用层次分析法和因子分析法确定洪涝灾害各项指标权重，然后运用模糊综合评价法（FCE）以及ArcGIS的建模功能对洪涝灾害风险进行分析。

（4）陕西省洪涝灾害风险评估。本文顾及地理学研究视角，借助地理图层中叠加综合的思想构建了洪涝灾害风险评估模型，将500m×500m格网单元与市域行政单元相结合展开风险评估，在格网单元的基础上利用加权法对市域行政单元洪涝灾害风险等级进行划分，通过陕西省洪涝灾害风险评估模型计算得到如下结论：陕西省洪涝灾害风险性整体上呈现“由南到北，依次递减”的趋势，且呈现“片状”、“带状”分布特征。其分布区域面积最大的是较低风险性地区，高风险性地区的面积最小，低风险性地区和较高风险性地区面积大致相等。陕南地区城市的风险等级主要是中等及以上，高风险区主要城市是商洛市和安康市；陕北地区城市主要呈现低风险及较低风险性等级；关中地区主要呈现低风险，较低风险及中等风险。在等级划分的基础上，提出陕西省洪涝灾害风险管理及优化策略，建立内涝预警系统，增强居民防灾意识，管理部门与政府各个部门之间明确分工，共同防范。

With the continuous change of the global climate and the acceleration of urbanization, extreme rainfall events in cities have gradually increased, and rainstorm and flood disasters have become one of the main natural disasters affecting regional social and economic development, people's lives and property safety. In the field of disaster research, disaster risk assessment is an extremely important non-engineering measure to deal with disasters. The assessment results can provide a scientific basis for relevant departments to formulate disaster prevention and mitigation plans, and can also quickly identify the spatial distribution of risks for emergency management departments and then make decisions. Provide important help. This paper takes Shaanxi Province as the research area, based on remote sensing data, gridded geographic background data, spatialized social and economic data, etc., and uses ArcGIS's spatial data processing, spatial analysis and model building functions to carry out flood disaster risk assessment and zoning. The research results are as follows:

(1) Analysis on the driving force of flood disaster impact factors. According to the occurrence of flood disasters in Shaanxi Province, the historical survey results of flood disasters were counted, and 10 types of driving factors were finally selected to quantitatively analyze the impact of each driving factor on flood disasters. The geographic detector model was used to analyze the driving force of flood disaster factors. , precipitation is the direct cause of flood disasters, and slope is the basic condition for flood disasters. Unreasonable human activities aggravate the occurrence of flood disasters. The combined effect of multiple factors on flood disasters will increase the frequency of flood disasters.

(2) Build a flood risk index evaluation system in Shaanxi Province. According to the actual situation of Shaanxi Province, based on the research results of previous scholars, combined with the principles of comprehensiveness and availability of data, we choose to construct a flood disaster assessment method in Shaanxi Province based on flood disaster exposure, risk, vulnerability, and disaster prevention and mitigation capabilities. A total of 12 evaluation indicators are selected, including population density, gross regional product, slope, river density, precipitation, cultivated land area, population of old and young, flood defense capability, density of shelters, monitoring capability, medical assistance capability, and residents’ self-rescue capability. As a secondary index, a risk index evaluation system is established.

(3) Determination of risk indicator weights. The Analytic Hierarchy Process (AHP) method is established based on the four parts of the analytic hierarchy process (AHP) based on the establishment of the system hierarchy, the construction of the judgment matrix, the single-level ranking and the consistency test of the judgment matrix, and the combination weighting method and the factor analysis method are used to determine the flood disaster The index weights are then used to analyze the flood disaster risk using Fuzzy Comprehensive Evaluation (FCE) and the modeling function of ArcGIS.

(4) Flood and waterlogging disaster risk assessment in Shaanxi Province. Taking into account the perspective of geographical research, this paper builds a flood disaster risk assessment model with the help of the idea of superposition and synthesis in geographic layers. The 500m×500m grid unit is combined with the city administrative unit to carry out risk assessment. The flood disaster risk level of the municipal administrative unit is divided by the method, and the following conclusions are obtained through the calculation of the flood disaster risk assessment model in Shaanxi Province: The risk of flood disasters in Shaanxi Province as a whole showed a distribution of "from south to north, decreasing in turn", and showed the characteristics of "sheet" and "band" distribution. The largest distribution area is the low-risk area, the high-risk area is the smallest, and the low-risk area and the high-risk area are roughly equal in size. The risk levels of cities in southern Shaanxi are mainly medium and above, and the main cities in high-risk areas are Shangluo and Ankang; Cities in northern Shaanxi mainly present low-risk and low-risk levels; Guanzhong area mainly presents low-risk, low-risk and medium-risk. On the basis of classification, it proposes flood risk management and optimization strategies in Shaanxi Province, establishes an early warning system for waterlogging, enhances residents' awareness of disaster prevention, and has a clear division of labor between management departments and various government departments to jointly prevent them.