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论文中文题名:

 暴雨洪涝灾害风险评估研究——以西安市临潼区为例    

姓名:

 朱贵玉    

学号:

 20209226094    

保密级别:

 保密(1年后开放)    

论文语种:

 chi    

学科代码:

 085700    

学科名称:

 工学 - 资源与环境    

学生类型:

 硕士    

学位级别:

 工程硕士    

学位年度:

 2023    

培养单位:

 西安科技大学    

院系:

 地质与环境学院    

专业:

 地质工程    

研究方向:

 自然灾害预测与防治理论    

第一导师姓名:

 方世跃    

第一导师单位:

 西安科技大学    

论文提交日期:

 2023-06-19    

论文答辩日期:

 2023-06-06    

论文外文题名:

 A Study on Risk Assessment of Rainstorm Flood Disasters: Taking Lintong District, Xi'an City as an Example    

论文中文关键词:

 暴雨洪涝 ; 评估模型 ; 风险评估 ; 防治建议    

论文外文关键词:

 rainstorm flood disasters ; assessment model ; risk assessment ; prevention and control recommendations    

论文中文摘要:

临潼区位于陕西省西安市,是西安市的重要经济发展区域之一。频繁发生的暴雨洪涝灾害对临潼区的生产生活和经济发展造成了巨大的影响,阻碍了临潼区的快速发展。因此,开展临潼区暴雨洪涝灾害风险评估具有重要意义。利用DEM、地形地貌、社会经济和气象等数据,经过对风险评估指标进行辨识,采用相关分析、逐步回归、模糊层次分析法、CRITIC法、组合赋权法、模糊综合评价法、自然灾害理论等方法,在指标筛选、指标赋权、评估方法等方面,通过不同方法组合,组建多个的风险评估模型。对评估模型结果进行合理性分析和可靠性检验后,确定出临潼区暴雨洪涝灾害风险评估的最优模型,获得临潼区暴雨洪涝灾害风险评估结果。同时,根据临潼区水库、湖泊、陂塘和堤坝的现状,提出相应的防治措施建议。主要取得以下成果:

(1)建立了基础指标体系、相关分析指标体系、逐步回归指标体系三种指标体系,分别计算得到模糊层次分析法、CRITIC法和组合赋权法三种方法下的指标权重,使用模糊综合评价法和自然灾害理论两种方法组建了18种风险评估模型。

(2)确定出临潼区暴雨洪涝灾害的最优模型,最优模型组合基于相关分析指标体系,采用模糊层次分析法进行指标赋权,利用模糊综合评价评估暴雨洪涝灾害风险等级。

(3)获得临潼区暴雨洪涝灾害风险分区:低风险区占临潼区总面积的18.88%,主要分布在南部山区;较低风险区占全区总面积的26.14%;中风险区占全区总面积的17.7%;较高风险区占全区总面积的29.91%,主要分布在何寨、新丰和交口等街道办;高风险区占全区总面积的7.36%,主要分布在斜口、零口和相桥等街道办。

(4)临潼区暴雨洪涝灾害的防治措施建议为:建议对铁炉街道办的水库、湖泊和陂塘定期进行巡查,对骊山街道办和秦陵街道办的堤坝运用防渗加固技术;加固加高DB01~DB023段河堤,提高防洪标准。对高风险区的防洪防涝工程进行详细调查,更新维护防治工程;进一步加强较高风险区的防灾减灾救灾宣传教育,提升市民防灾减灾意识和自救互救技能;加强高风险和较高风险区防洪设施、设备和防洪人员储备,降低灾害带来的损失。

论文外文摘要:

Lintong District is located in Xi'an City, Shaanxi Province, and is one of the important economic development areas of Xi'an. Frequent rainstorm flood disasters have caused great impact on the production and life and economic development of Lintong District, and hindered the rapid development of Lintong District. Therefore, it is important to carry out the risk assessment of rainstorm flood disasters in Lintong District. Using the data of DEM, topography and geomorphology, socio-economics and meteorology, after identifying the risk assessment indexes, multiple risk assessment models are formed by combining different methods such as correlation analysis, stepwise regression, fuzzy hierarchical analysis, CRITIC method, combined assignment method, fuzzy comprehensive evaluation method and natural disaster theory, in terms of index screening, index assignment and assessment methods. After analyzing the results of the assessment models for reasonableness and reliability, the optimal model for the risk assessment of rainstorm flood disasters in Lintong District was determined, and the risk assessment results of rainstorm flood disasters in Lintong District were obtained. Meanwhile, according to the current situation of reservoirs, lakes, ponds and dykes in Lintong District, corresponding prevention and control measures are proposed. The main achievements are as follows:

(1) The three indicator systems of basic index system, correlation analysis index system and stepwise regression index system were established, the index weights under the three methods of fuzzy hierarchical analysis method, CRITIC method and combined assignment method were calculated respectively, and 18 risk assessment models were formed using two methods of fuzzy comprehensive evaluation method and natural disaster theory.

(2) Determine the optimal model for rainstorm flood disasters in Lintong District. The optimal model combination is based on the relevant analysis index system, and the fuzzy hierarchical analysis method is used to assign the index weights, and the fuzzy comprehensive evaluation is used to assess the risk level of rainstorm flood disasters.

(3) To obtain the risk zoning of rainstorm flood disasters in Lintong District: low risk zone accounts for 18.88% of the total area of Lintong District, mainly distributed in the southern mountainous area; lower risk zone accounts for 26.14% of the total area of the district; medium risk zone accounts for 17.7% of the total area of the district; higher risk zone accounts for 29.91% of the total area of the district, mainly distributed in Hezhai, Xinfeng and Jiaokou street offices; high risk zone accounts for The high-risk area accounts for 7.36% of the total area of the district, mainly distributed in the street offices of Xiekou, Zero and Xiangqiao.

(4) The prevention and control measures for rainstorm flood disasters in Lintong District are as follows: it is recommended to conduct regular inspections of reservoirs, lakes and ponds in Tielu Street Office, and to apply seepage prevention and reinforcement technology to the dikes in Lishan Street Office and Qinling Street Office; to reinforce and raise the river dikes in DB01~DB023 section to improve the flood prevention standard. Conduct a detailed survey of flood control and prevention projects in high-risk areas and update and maintain prevention and control projects; further strengthen disaster prevention, mitigation and relief education in higher-risk areas to enhance citizens' awareness of disaster prevention and mitigation and self-help and mutual rescue skills; strengthen flood control facilities, equipment and flood control personnel reserves in high-risk and higher-risk areas to reduce the losses caused by disasters.

参考文献:

[1] 史瑞琴, 刘宁, 李兰, 等. 暴雨洪涝淹没模型在洪灾损失评估中的应用 [J]. 暴雨灾害, 2013, 32(04): 379-384.

[2] 徐宗学, 陈浩, 任梅芳, 等. 中国城市洪涝致灾机理与风险评估研究进展 [J]. 水科学进展, 2020, 31(05): 713-724.

[3] 王博, 崔春光, 彭涛, 等. 暴雨灾害风险评估与区划的研究现状与进展 [J]. 暴雨灾害, 2007, 26(03): 281-286.

[4] 黄大鹏, 刘闯, 彭顺风. 洪灾风险评价与区划研究进展 [J]. 地理科学进展, 2007, 26(04): 11-22.

[5] Zerger Andre, Wealands Stephen. Beyond Modelling: Linking Models with GIS for Flood Risk Management [J]. Natural Hazards, 2004, 33(2): 191-208.

[6] Zhao Yue, Gong Zaiwu, Wang Wenhao, et al. The comprehensive risk evaluation on rainstorm and flood disaster losses in China mainland from 2004 to 2009: based on the triangular gray correlation theory [J]. Natural Hazards, 2014, 71(2): 1001-1016.

[7] Liu Jiafu, Wang Xinquan, Zhang Bai, et al. Storm flood risk zoning in the typical regions of Asia using GIS technology [J]. Natural Hazards, 2017, 87(3): 1691-1707.

[8] Asare-Kyei Daniel, Renaud Fabrice G., Kloos Julia, et al. Development and validation of risk profiles of West African rural communities facing multiple natural hazards [J]. Plos One, 2017, 12(3): 26.

[9] Arnous Mohamed O., Omar Ali E. Hydrometeorological hazards assessment of some basins in Southwestern Sinai area, Egypt [J]. Journal of Coastal Conservation, 2018, 22(4): 721-743.

[10] Mahmood Shakeel, Rahman Atta-ur, Sajjad Asif. Assessment of 2010 flood disaster causes and damages in district Muzaffargarh, Central Indus Basin, Pakistan [J]. Environmental Earth Sciences, 2019, 78(3): 11.

[11] Azareh Ali, Sardooi Elham Rafiei, Choubin Bahram, et al. Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment [J]. Geocarto International, 2021, 36(20): 2345-2365.

[12] Wu Zening, Shen Yanxia, Wang Huiliang. Assessing Urban Areas Vulnerability to Flood Disaster Based on Text Data: A Case Study in Zhengzhou City [J]. Sustainability, 2019, 11(17): 15.

[13] Chen Junfei, Ji Juan, Wang Huimin, et al. Risk Assessment of Urban Rainstorm Disaster Based on Multi-Layer Weighted Principal Component Analysis: A Case Study of Nanjing, China [J]. International Journal of Environmental Research and Public Health, 2020, 17(15): 19.

[14] Su Xin, Shao Weiwei, Liu Jiahong, et al. Dynamic Assessment of the Impact of Flood Disaster on Economy and Population under Extreme Rainstorm Events [J]. Remote Sensing, 2021, 13(19): 21.

[15] Romali Noor Suraya, Yusop Zulkifli. Flood damage and risk assessment for urban area in Malaysia [J]. Hydrology Research, 2021, 52(1): 142-159.

[16] Chen Junfei, Liu Liming, Pei Jinpeng, et al. An ensemble risk assessment model for urban rainstorm disasters based on random forest and deep belief nets: a case study of Nanjing, China [J]. Natural Hazards, 2021, 107(3): 2671-2692.

[17] Ma Shuqi, Lyu Shuran, Zhang Yudong. Weighted clustering-based risk assessment on urban rainstorm and flood disaster [J]. Urban Climate, 2021, 39: 100974.

[18] 张行南, 罗健, 陈雷, 等. 中国洪水灾害危险程度区划 [J]. 水利学报, 2000, (03): 3-9.

[19] 李林涛, 徐宗学, 庞博, 等. 中国洪灾风险区划研究 [J]. 水利学报, 2012, 43(01): 22-30.

[20] 杨帅, 苏筠. 县域暴雨洪涝灾害损失快速评估方法探讨——以湖南省为例 [J]. 自然灾害学报, 2014, 23(05): 156-163.

[21] 戴娟, 潘益农, 刘青, 等. 改进的AHP在县域尺度暴雨洪涝风险评价的应用 [J]. 气象科学, 2014, 34(04): 428-434.

[22] 戚蓝, 李楠. 基于事故树—层次分析法相耦合的山洪灾害防御系统评价分析 [J]. 水利水电技术, 2017, 48(04): 141-145.

[23] 蒋新宇, 范久波, 张继权, 等. 基于GIS的松花江干流暴雨洪涝灾害风险评估 [J]. 灾害学, 2009, 24(03): 51-56.

[24] 魏一鸣, 张林鹏, 范英. 基于Swarm的洪水灾害演化模拟研究 [J]. 管理科学学报, 2002, (06): 39-46.

[25] 黄涛珍, 王晓东. BP神经网络在洪涝灾损失快速评估中的应用 [J]. 河海大学学报(自然科学版), 2003, (04): 457-460.

[26] 王倩雯, 曾坚, 辛儒鸿. 基于GIS多准则评价与BP神经网络的暴雨洪涝灾害风险辨识——以闽三角地区为例 [J]. 灾害学, 2021, 36(01): 192-200.

[27] 张杰, 吴明业. 基于GIS的皖南地区暴雨洪涝灾害风险区划 [J]. 中国农业资源与区划, 2017, 38(06): 121-129.

[28] 石涛, 谢五三, 张丽, 等. 暴雨洪涝风险评估的GIS和空间化应用——以芜湖市为例 [J]. 自然灾害学报, 2015, 24(05): 169-176.

[29] 彭建, 魏海, 武文欢, 等. 基于土地利用变化情景的城市暴雨洪涝灾害风险评估——以深圳市茅洲河流域为例 [J]. 生态学报, 2018, 38(11): 3741-3755.

[30] 邹德全, 邹承立, 田洪进, 等. 基于信息扩散技术的遵义市暴雨洪涝灾害风险评估 [J]. 安全与环境学报, 2022, 22(04): 2070-2077.

[31] 李琳, 朱秀芳, 孙章丽, 等. 辽宁省暴雨洪涝灾害风险评估模型的建立与应用 [J]. 北京师范大学学报(自然科学版), 2015, 51(S1): 49-56.

[32] 万昔超, 殷伟量, 孙鹏, 等. 基于云模型的暴雨洪涝灾害风险分区评价 [J]. 自然灾害学报, 2017, 26(04): 77-83.

[33] 方建, 李梦婕, 王静爱, 等. 全球暴雨洪水灾害风险评估与制图 [J]. 自然灾害学报, 2015, 24(01): 1-8.

[34] 李万志, 余迪, 冯晓莉, 等. 基于风险度的青海省暴雨洪涝灾害风险评估 [J]. 冰川冻土, 2019, 41(03): 680-688.

[35] 黄国如, 李碧琦. 基于模糊综合评价的深圳市暴雨洪涝风险评估 [J]. 水资源与水工程学报, 2021, 32(01): 1-6.

[36] 莫建飞, 陆甲, 李艳兰, 等. 基于GIS的广西农业暴雨洪涝灾害风险评估 [J]. 灾害学, 2012, 27(01): 38-43.

[37] 李喜仓, 白美兰, 杨晶, 等. 基于GIS技术的内蒙古地区暴雨洪涝灾害风险区划及评估研究 [J]. 干旱区资源与环境, 2012, 26(07): 71-77.

[38] 陈长坤, 孙凤琳. 基于熵权-灰色关联度分析的暴雨洪涝灾情评估方法 [J]. 清华大学学报(自然科学版), 2022, 62(06): 1067-1073.

[39] 高超, 张正涛, 刘青, 等. 承灾体脆弱性评估指标的最优格网化方法——以淮河干流区暴雨洪涝灾害为例 [J]. 自然灾害学报, 2018, 27(03): 119-129.

[40] 符洪恩, 高艺桔, 冯莹莹, 等. 基于GA-SVR-C的城市暴雨洪涝灾害危险性预测——以深圳市为例 [J]. 人民长江, 2021, 52(08): 16-21.

[41] 苑希民, 桑林浩, 沈福新, 等. 基于模糊层次分析法的京津冀洪灾风险评价 [J]. 水利水电技术, 2018, 49(10): 37-45.

[42] 张薇, 黄海荣, 董润润, 等. 基于模糊数学分析法的暴雨洪涝灾害风险评估 [J]. 河南科技, 2023, 42(03): 104-108.

[43] 刘安梦云, 韩艳, 展润青, 等. 基于模糊综合评价的城市暴雨洪涝风险评估——以河南省新乡市为例 [J]. 测绘, 2022, 45(05): 225-229.

[44] 张吉军. 模糊层次分析法(FAHP) [J]. 模糊系统与数学, 2000, (02): 80-88.

[45] 兰继斌, 徐扬, 霍良安, 等. 模糊层次分析法权重研究 [J]. 系统工程理论与实践, 2006, (09): 107-112.

[46] 李永, 胡向红, 乔箭. 改进的模糊层次分析法 [J]. 西北大学学报(自然科学版), 2005, (01): 11-12+16.

[47] Danae Diakoulaki, Georges Mavrotas, Lefteris Papayannakis. Determining objective weights in multiple criteria problems: The critic method [J]. Computers & Operations Research, 1995, 22(7): 763-770.

[48] 刘秋艳, 吴新年. 多要素评价中指标权重的确定方法评述 [J]. 知识管理论坛, 2017, 2(06): 500-510.

[49] 韩利, 梅强, 陆玉梅, 等. AHP-模糊综合评价方法的分析与研究 [J]. 中国安全科学学报, 2004, (07): 89-92+83.

[50] 安洋洋. 基于多源数据的岳阳市洪涝灾害风险评估研究 [D]. 北京: 中国地质大学, 2021.

[51] 孙建霞. 基于GIS和RS技术的吉林省暴雨洪涝灾害风险评价 [D]. 长春: 东北师范大学, 2010.

[52] Yue Tianxiang, Chen shupeng, Xu Bing, et al. A curve-theorem based approach for change detection and its application to Yellow River Delta [J]. International Journal of Remote Sensing, 2002, 23(11): 2283-2292.

[53] 李小曼, 王刚. 黄土丘陵沟壑区地形分类方法的研究 [J]. 测绘科学, 2009, 34(03): 132-133.

[54] 曹卫斌, 叶朋, 赵慧. 全国河流的密度统计方法 [J]. 水利水电工程设计, 2015, 34(02): 53-56.

[55] 师长兴, 周园园, 范小黎, 等. 利用DEM进行黄河中游河网提取及河网密度空间差异分析 [J]. 测绘通报, 2012, (10): 24-27.

[56] 王海力, 韩光中, 谢贤建. 单流向法地形湿度指数尺度效应的不同地形区差异分析 [J]. 地理与地理信息科学, 2016, 32(04): 23-29+127.

[57] Liu Honghui, Jiang Dong, Yang Xiaohuan, et al. Spatialization approach to 1 km grid GDP supported by remote sensing [J]. Geo-Inf Sci, 2005, 7: 120-123.

[58] Yi Ling, Xiong Liya, Yang Xiaohuan, et al. Method of pixelizing GDP data based on the GIS. [J]. J Gansu Sci, 2006, 18: 54-58.

[59] 黄莹 包安明, 陈曦, 刘海隆, 杨光华. 基于绿洲土地利用的区域 GDP 公里格网化研究 [J]. 冰川冻土, 2009, (1): 158-165.

[60] 王灿. (2022). 中国历史GDP空间分布公里网格数据集(1990-2015)[EB/OL] 国家青藏高原科学数据中心]. https://doi.org/10.12078/2017121102.

[61] 徐新良. (2017). 中国GDP空间分布公里网格数据集[EB/OL] 资源环境科学数据注册与出版系统]. http://www.resdc.cn/DOI).DOI:10.12078/2017121102.

[62] 刘海, 刘凤, 郑粮, 等. 风险分析与损失评估相结合的北方城市洪涝灾害研究——以郑州市2021年7月特大暴雨洪涝灾害为例 [J]. 华中师范大学学报(自然科学版), 2023, 57(01): 59-68.

[63] 张利平, 蔡松, 张鸿, 等. 达川区暴雨洪涝灾害风险区划 [C]// 中国气象学会. 第35届中国气象学会年会 S10 水文气象灾害形成机理、预报预测预警与风险评估新技术, 2018:8.

[64] 蒲明芳, 李天涛, 裴向军, 等. 粉质土斜坡降雨侵蚀产沙过程及其动力学机制 [J]. 工程地质学报: 1-15.

[65] 王瑞红, 李明鑫, 张瀚, 等. 不同植被盖度对三峡库区边坡减蚀的室内模拟降雨研究 [J]. 水土保持学报, 2023, 37(01): 59-64.

[66] 杜家涛. 甘肃省白银区地质灾害风险评价研究 [D]. 郑州: 华北水利水电大学, 2022.

[67] 马世发, 马梅, 蔡玉梅, 等. 面向国土规划的空间评价标准地域单元划分 [J]. 地域研究与开发, 2015, 34(03): 112-117.

[68] 潘赟, 丛威青, 潘懋. 基于GIS的辽宁省岫岩县泥石流灾害危险性区划研究 [J]. 北京大学学报(自然科学版), 2010, 46(04): 601-606.

[69] 武雪玲, 任福, 牛瑞卿, 等. 斜坡单元支持下的滑坡易发性评价支持向量机模型 [J]. 武汉大学学报(信息科学版), 2013, 38(12): 1499-1503.

[70] 唐晓娜. 基于卷积神经网络和综合指数模型的吕梁市滑坡灾害易发性评价 [D]. 太原: 太原理工大学, 2019.

[71] 陈敦隆. 相关分析基本内容及其应用 [J]. 数学通报, 1960, (03): 8-9+32.

[72] 许树柏. 实用决策方法—层次分析法原理 [M]. 天津: 天津大学出版社, 1988.

[73] 潘文燕. 密涿高速公路北京山区段地质灾害特征及危险性评价 [D]. 北京: 中国地质大学, 2015.

[74] 章国材. 自然灾害风险评估与区划原理和方法 [M]. 北京: 气象出版社, 2013.

中图分类号:

 P426.616    

开放日期:

 2024-06-19    

无标题文档

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