基于ArcGIS的延长县区域滑坡灾害易发性评价

发布时间：2018-06-03 22:03

本文选题：滑坡灾害 + 斜坡单元　；参考：《长安大学》2017年硕士论文

【摘要】：滑坡是我国主要地质灾害之一,严重制约着国民经济的发展。延长县位于陕西省延安市东部,地处陕北黄土高原,全县几乎都被不同厚度的黄土所覆盖,属于典型的黄土高原地貌。研究区域内丘陵沟壑纵横,地质环境脆弱,地质灾害时有发生,尤其以黄土滑坡灾害为著,是陕西省地质灾害易发区之一。根据野外调查研究显示,延长县共发生滑坡灾害83例,其中小型滑坡45例,中型滑坡37例,大型滑坡1例,没有特大型及巨型滑坡,但也给延长县人民生命财产安全构成了严重威胁。对滑坡灾害进行易发性评价,做到预险于前、防患未然,是防灾减灾的一项重要举措。本文以延长县为研究区域,通过分析已发滑坡的自然和地理环境因子,分别从致灾因子提取、剖分单元的确定及评价模型的选择等几方面进行滑坡易发性评价分析,主要完成的研究工作和成果如下:(1)以ArcGIS为平台整合研究区相关数据,应用统计分析方法确定各种影响因素对历史滑坡的重要程度,最终筛选出岩性、坡度、坡高、坡向、地貌和黄土厚度6种因素为易发性评价因子,并以滑坡密度值(LC)为指标实现因子统一量化。(2)以斜坡单元和栅格单元为评价单元,应用层次分析法进行滑坡易发性评价,完成延长县滑坡易发等级区划图。将区划图与研究区历史滑坡分布图进行对比分析,发现基于斜坡单元的评价结果比栅格单元更加可靠,说明斜坡单元更适合研究区滑坡易发性评价。(3)以斜坡单元为评价单元,分别应用逻辑回归模型和BP神经网络模型再次进行易发性评价,并与层次分析法评价结果进行比较,发现三种评价模型的评价结果总体趋势保持一致。相对而言,BP神经网络模型可靠性最高,层次分析法次之,逻辑回归模型可靠性相对偏低。(4)通过分析易发等级区划图发现,研究区高易发区主要集中于县区西部延河及其支流沿岸附近地区和北部小部分地区;低易发区主要集中于东部区域和中部远离河流主干的区域。
[Abstract]:Landslide is one of the main geological disasters in China, which seriously restricts the development of national economy. Yanchang County is located in the eastern part of Yan'an City of Shaanxi Province and is located in the Loess Plateau of Northern Shaanxi Province. Almost all the counties are covered by loess with different thickness, which is a typical landform of Loess Plateau. The hilly and gully region in the study area, the geological environment is fragile, geological disasters occur from time to time, especially the loess landslide disaster, is one of the prone areas of geological disasters in Shaanxi Province. According to field investigation, 83 cases of landslide disasters occurred in Yanchang County, including 45 cases of small landslide, 37 cases of medium landslide and 1 case of large landslide. However, it also poses a serious threat to the safety of people's lives and property in Yanchang County. It is an important measure of disaster prevention and mitigation to evaluate the vulnerability of landslide disasters, and to prevent and reduce disasters. This paper takes Yanchang County as the study area, through analyzing the natural and geographical environmental factors of the landslide, respectively, from the aspects of the extraction of the disaster factors, the determination of the subdivision unit and the selection of the evaluation model, etc., the landslide susceptibility evaluation analysis is carried out. The main research work and results are as follows: 1) using ArcGIS as the platform to integrate the relevant data in the research area, using statistical analysis methods to determine the importance of various factors to the historical landslide, and finally screening out the lithology, slope gradient, slope height, slope direction, Six factors of geomorphology and loess thickness are the factors of vulnerability evaluation, and the landslide density value (LCL) is taken as the index to realize the unified quantification of the factors. (2) the slope unit and the grid unit are taken as the evaluation units, and the analytic hierarchy process is applied to evaluate the landslide susceptibility. To complete the Yanchang County landslide prone grade zoning map. By comparing the zoning map with the historical landslide distribution map in the study area, it is found that the evaluation results based on the slope unit are more reliable than that of the grid unit, which indicates that the slope unit is more suitable for the evaluation of landslide vulnerability in the study area. By using the logical regression model and the BP neural network model, the susceptibility evaluation is carried out again, and compared with the analytic hierarchy process (AHP), it is found that the overall trend of the evaluation results of the three evaluation models is consistent. Comparatively speaking, the reliability of BP neural network model is the highest, the analytic hierarchy process is the second, and the reliability of the logical regression model is relatively low. The high susceptibility areas in the study area are mainly concentrated in the areas near Yanhe River and its tributaries in the west of the county area and a small part in the north, while the low susceptibility areas are mainly concentrated in the eastern region and the central region far from the main river trunk.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P642.22

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