帮助 关于我们

返回检索结果

中巴经济走廊地质灾害敏感性分析
SUSCEPTIBILITY ANALYSIS OF GEO-HAZARDS IN CHINA-PAKISTAN ECONOMIC CORRIDOR

查看参考文献70篇

裴艳茜 1,2   邱海军 1,2,3 *   胡胜 1,2,3   邹强 4   杨冬冬 1,2   张焱 1,2   曹明明 1  
文摘 中巴经济走廊是“一带一路”建设的旗舰项目,对中巴经济走廊地质灾害进行敏感性分析与区划,可为中巴经济走廊地质灾害的防治提供参考。基于此,文章选取高程、坡度、地形起伏度、地震密度、多年平均降雨量、距断裂带的距离、地层岩性、距河流的距离和距公路的距离等9个指标,采用加权信息量模型和证据权法,综合分析中巴经济走廊地质灾害敏感性。并运用ROC曲线验证对比两种模型分析的敏感性结果,选取精度较高的模型分析的地质灾害敏感性进行研究区区划,最后对区划结果进行相关验证。研究结果表明:1)中巴经济走廊容易诱发地质灾害的条件为:高程在2660~ 3470 m之间,坡度为29°~35°,地形起伏度在45 ~ 70 m之间,地震密度在2~ 4个/102 km~2之间,多年平均降雨量在430~530 mm之间,距断裂带距离小于5 km,地层岩性为软岩和极软岩,距河流距离小于2 km,距公路距离小于2 km。2)基于加权信息量模型和证据权法分析的中巴经济走廊地质灾害敏感性精度分别为0.821和0.795,表明本研究选取的地质灾害敏感性分析模型具有较高的准确性,但是运用加权信息量模型分析的敏感性更符合实际情况。因此,运用加权信息量模型分析的结果进行研究区地质灾害敏感性区划。3)研究区地质灾害不敏感区、低敏感区、中等敏感区、高度敏感区和极高敏感区所占面积比分别为26.3%、23.1%、21.6%、18.8%和10.2%,以中、低敏感性为主;且敏感性呈现出南低北高的空间分布特征。4)运用实地调研的地质灾害点对本文区划的中巴经济走廊地质灾害敏感性结果验证,表明区划的结果具有一定的科学、可信性,可以为中巴经济走廊重大工程建设、交通线路选线、城市规划、资源开发等方面提供科学依据。
其他语种文摘 The China-Pakistan Economic Corridor (CPEC) is a channel consisting of roads, railways, oil and gas pipelines, optical cables and electric power lines. From the north of Kashgar Prefecture of China to the south of Gwadar Port of Pakistan, the total length of the corridor is 3,000 km. The CPEC is guided by the Karakoram Highway(KKH),which runs through the Pamirs Plateau. As a bridge of the Silk Road Economic Belt and the 21st Century Maritime Silk Road, the corridor plays an important role in collaborating among China, South Asia, and the Middle East. Also, the CPEC is the pilot and the major project of the Belt and Road initiative. On the other hand,considering the wide areas the CPEC covers,it is important to take the great differences of diversities in to account, especially considering the natural environment, complex geological conditions, frequent seismic activities, and extremely poor regional stability,resulting in many geo-hazards in the region, and widespread mountainous disasters in the region, which is seriously threatening the construction and livelihood security of the CPEC. Therefore, scientific and accurate analysis of the susceptibility of geo-hazards in this region has become a major and urgent practical issue facing the construction of the CPEC. Based on the background, this paper selects nine indexes of elevation, slope, terrain relief, seismic density, multi-year average rainfall, distance from the fault, lithology, distance from the river, and distance from the road. The weighted information model and weight of evidence model to comprehensive analysis the susceptibility of geohazards in the CPEC. Using a ROC curve to verify the results of the two models, and to select more accurate results for the study area susceptible division. The several results indicated in the research are going to be discussed in the following passage. First of all, conditions that are likely to induce geo-hazards are: elevations between 2660 m and 3470 m, slopes between 29° and 35°, terrain relief between 45 m and 70 m, density of the earthquake between 2 and 4 per 10~2 km~2,multi-year average rainfall between 430 mm and 530 mm,distance from the fault is less than 5 km, conditions such as the lithology is soft and softest lithology,the distance from the river is less than 2 km,and the distance from the road is also less than 2 km likely to induce the geo-hazards. In addition,the susceptibility of geohazards in the CPEC is negatively related to the distance from rivers, roads, fault zones and lithology. The condition where is closer to river, road and fault is easier, the lithology is the softer, the more likely to cause geo-hazards. The seismic density shows a positive correlation with geo-hazards, the greater the seismic density, the higher the frequency of geo-hazards. Elevation, slope, topographic relief, and multi-year average rainfall do not show a full positive or negative correlation to geo-hazards, but play a role within a specific range. Secondly, the susceptibility accuracy of geo-hazards in CPEC based on the weighted information model and weight of evidence model is 0.821 and 0.795,respectively. It can be shown that the weighted information model assesses the result has high accuracy, therefore the result based on weighted information model would be used to zone.
来源 第四纪研究 ,2018,38(6):1369-1383 【核心库】
DOI 10.11928/j.issn.1001-7410.2018.06.05
关键词 地质灾害 ; 敏感性 ; 加权信息量模型 ; 证据权法 ; 中巴经济走廊
地址

1. 西北大学城市与环境学院, 陕西, 西安, 710127  

2. 陕西省地表系统与环境承载力重点实验室, 陕西省地表系统与环境承载力重点实验室, 陕西, 西安, 710127  

3. 西北大学地表系统与灾害研究院, 陕西, 西安, 710127  

4. 中国科学院成都山地灾害与环境研究所, 中国科学院山地灾害与地表过程重点实验室, 四川, 成都, 610041

语种 中文
文献类型 研究性论文
ISSN 1001-7410
学科 地质学
基金 科技部国家重点研发计划政府间国际科技创新合作重点专项项目
文献收藏号 CSCD:6372620

参考文献 共 70 共4页

1.  朱颖彦. 中巴喀喇昆仑公路冰川地貌地质灾害. 灾害学,2014,29(3):81-90 被引 20 次    
2.  李安. 汶川M_s8.0地震平原区地表破裂的初步调查. 第四纪研究,2009,29(3):554-564 被引 2 次    
3.  聂高众. 21世纪中国的自然灾害发展趋势──以地震和旱涝灾害为例. 第四纪研究,2001,21(3):249-261 被引 17 次    
4.  邱海军. 黄土滑坡灾害空间格局及其空间尺度依赖性研究. 第四纪研究,2017,37(2):307-318 被引 11 次    
5.  Vernes D J. Landslide hazard zonation: A review of principle and practice. Natural Hazards,1984(3):1-63 被引 1 次    
6.  Baeza C. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes & Landforms,2010,26(12):1251-1263 被引 25 次    
7.  Corominas J. Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology & the Environment,2014,73(2):209-263 被引 38 次    
8.  Ermini L. Artificial neural networks applied to landslide susceptibility assessment. Geomorphology,2005,66(1):327-343 被引 24 次    
9.  Falaschi F. Logistic regression versus artificial neural networks: Landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Natural Hazards,2009,50(3):551-569 被引 4 次    
10.  Rossi M. Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology,2010,114(3):129-142 被引 22 次    
11.  Trigila A. Quality assessment of the Italian landslide inventory using GIS processing. Landslides,2010,7(4):455-470 被引 8 次    
12.  Westen C J V. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology,2008,102(3):112-131 被引 52 次    
13.  Chung C J F. Validation of spatial prediction models for landslide hazard mapping. Natural Hazards,2003,30(3):451-472 被引 36 次    
14.  Brenning A. Spatial prediction models for landslide hazards: Review, comparison and evaluation. Natural Hazards & Earth System Sciences,2005,5(6):853-862 被引 18 次    
15.  Carrara A. Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology,2008,94(3):353-378 被引 18 次    
16.  Frattini P. Techniques for evaluating the performance of landslide susceptibility models. Engineering Geology,2009,111(1):62-72 被引 17 次    
17.  Conforti M. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena,2014,113(1):236-250 被引 21 次    
18.  邱海军. 区域滑坡崩塌地质灾害特征分析及其易发性和危险性评价研究. 博士论文,2012:90-93 被引 1 次    
19.  Aqeel M. Impact of China Pakistan Economic Corridor (CPEC),2016:7-9 被引 1 次    
20.  Irshad M S. One Belt and One Road: Dose China-Pakistan Economic Corridor benefit for Pakistan's economy?. Social Science Electronic Publishing,2015,6(24):200-207 被引 1 次    
引证文献 6 篇

1 杨文璐 典型黄土丘陵区浅层黄土滑坡稳定性评价——以延安市志丹县为例 第四纪研究,2019,39(2):408-419
被引 4 次

2 何简吟 基于TRIGRS模型的浅层滑坡稳定性分析 第四纪研究,2019,39(5):1222-1234
被引 1 次

显示所有6篇文献

论文科学数据集

1. 1995-2020年福建省地震孕灾环境敏感性评估数据

2. 1995-2020年城村汉城遗址洪涝孕灾环境危险性评估数据

3. 1995-2020年福建省洪涝孕灾环境敏感性评估数据

数据来源：
国家对地观测科学数据中心
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护：中国科学院文献情报中心
地址：北京中关村北四环西路33号 邮政编码：100190 联系电话：(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号