帮助 关于我们

返回检索结果

霍西煤矿区地表滑坡灾害敏感性数值建模与等级划分
Numerical Modeling and Degree Division to Landslide Susceptibility in the Ground Surface of Huoxi Coal Mine Area

查看参考文献28篇

苏巧梅 1   赵尚民 1 *   郭建立 2  
文摘 本文以山西省霍西煤矿区为研究区,利用遥感和GIS方法对滑坡灾害的敏感性进行了数值建模与定量评价。利用交叉检验方法构建了径向基核函数支持向量机滑坡敏感性评价模型,并基于拟合精度对模型进行了定量评价;对各评价因子在模型中的重要性进行对比分析;基于空间分辨率为30 m的评价因子,通过径向基核函数支持向量机模型获得了霍西煤矿区滑坡敏感性指数值,并利用分位数法将霍西煤矿区的滑坡敏感性分为极高、高、中和低4个等级。结果表明:拟合精度建模阶段和验证阶段分别为87.22%和70.12%;与滑坡敏感性关系最密切的5个评价因子依次是岩性、距道路距离、坡向、高程和土地利用类型;极高和高敏感区域分布了93.49%的滑坡点,面积占总面积的50.99%,是比较合理的分级方案。本研究不仅可以为研究区人工边坡调查和煤矿资源合理开采提供借鉴,对相似矿区的相关工作也具有参考价值。
其他语种文摘 Taking Huoxi Coal Mine Area in Shanxi Province as the research area, we conducted numerical modeling and quantitative evaluation of landslide susceptibility using remote sensing and GIS technology. Based on the DEM with spatial resolution of 30 m × 30 m, five topographical parameters were derived: elevation, slope angle, slope aspect, plan curvature and profile curvature. Stratigraphic lithology was digitized based on the geological maps from Department of Geological Survey in 1:50 000 scale. Fault network, drainage network and road were digitized based on the geological maps and other thematic maps from Department of Land Resource in 1:50 000 scale. Then, buffer for faults, drainage, and road were done. Mining disturbance were digitized based on the planning maps of coal resources. If the point falls in the mine area, it is proved to be disturbed by the mining disturbance, otherwise is not affected. NDVI and land-use types interpreted and computed the Landsat TM images. Landslide data was collected by Bureau of Land and Resources and it is represented by the X, Y coordinates of its central point. Then, the correlation characteristics among evaluation factors and the spatial distribution of landslides were acquired by using remote sensing technology and GIS spatial analysis method. Repeated 5-fold cross validation method was adopted in this research and the landslide/non-landslide datasets were randomly split into a ratio of 80:20 for training and validating models. Based on the methods of the 5-fold cross-validation and the fitting accuracy to the constructed the landslide susceptibility assessment model-Radial Basis Function-Support Vector Machine (RBF-SVM), the precision of the models was quantitatively assessed. We calculated the importance of each evaluation factor in the RBF-SVM model. Meanwhile, we obtained landslide susceptibility map of Huoxi Coal Mine Area based on the RBF-SVM model. The landslide susceptibility of Huoxi Coal Mine Area was divided into four scales referencing the quantile law: low (0-0.02), medium (0.02-0.1), high (0.1-0.85) and very high (0.85-1) probability of landslide. The results show that: (1) the fitting accuracy was 87.22% in the modeling phase and 70.12% in the validation phase, respectively, for the RBF-SVM model; (2) it indicated that lithology, distance from road, slope aspect, elevation and land-use types have contribution to each model. Therefore, these five factors are most suitable conditioning factors for landslide susceptibility mapping in this area. Mining disturbance factors have little contribution to the model. The mining method in this area is underground mining and the mining depth is very deep affecting the stability of the slopes. (3) The number of landslides points in the very high region was 316, which account for 93.49% of the total number of landslides points and 50.99% of the total area. This study obtained the spatial distribution characteristics of the Huoxi Coalfield geological disasters and the quantitative evaluation of landslide susceptibility. It provides reference for the investigation about artificial slope in the research area monitoring the rational mining coal resources. It will also provide the reference for the related research in other similar coal region and management work.
来源 地球信息科学学报 ,2017,19(12):1613-1622 【核心库】
DOI 10.3724/SP.J.1047.2017.01613
关键词 滑坡敏感性 ; GIS ; 支持向量机 ; 交叉验证 ; 霍西煤矿区
地址

1. 太原理工大学矿业工程学院测绘科学与技术系, 太原, 030024  

2. 山西省地质环境监测中心, 太原, 030024

语种 中文
文献类型 研究性论文
ISSN 1560-8999
学科 地质学;矿业工程
基金 国家重点研发计划"地球观测与导航"专项 ;  国家自然科学基金项目
文献收藏号 CSCD:6140919

参考文献 共 28 共2页

1.  何芳. 中国矿山地质灾害分布特征. 地质通报,2012,31(2):476-485 CSCD被引 18 次    
2.  吴树仁. 滑坡风险评估理论与技术,2012 CSCD被引 32 次    
3.  Westen C J. Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment. Natural Hazards,2003,30(3):399-419 CSCD被引 27 次    
4.  Xu W. Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China). Natural Hazards,2013,65(3):1379-1392 CSCD被引 3 次    
5.  牛瑞卿. 利用数据挖掘的滑坡监测数据处理流程. 武汉大学学报·信息科学版,2012,37(7):869-872 CSCD被引 7 次    
6.  Yao X. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China. Geomorphology,2008,101(4):572-582 CSCD被引 44 次    
7.  胡德勇. GIS支持下滑坡灾害空间预测方法研究. 遥感学报,2007,11(6):852-859 CSCD被引 26 次    
8.  戴福初. 滑坡灾害空间预测支持向量机模型及其应用. 地学前缘,2007,14(6):153-159 CSCD被引 25 次    
9.  许冲. 基于不同核函数的2010年玉树地震滑坡空间预测模型研究. 地球物理学报,2012,55(9):2294-3005 CSCD被引 1 次    
10.  谭龙. 逻辑回归与支持向量机模型在滑坡敏感性评价中的应用. 工程地质学报,2014,22(1):56-63 CSCD被引 20 次    
11.  Kavzoglu T. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides,2014,11(3):425-439 CSCD被引 39 次    
12.  Colkesen I. Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. Journal of African Earth Sciences,2016,118:53-64 CSCD被引 16 次    
13.  Pradhan B. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences,2013,51(2):350-365 CSCD被引 71 次    
14.  肖和平. 我国煤矿的主要地质灾害及防治对策. 中国地质灾害与防治学报,2001,12(1):51-54 CSCD被引 2 次    
15.  翟文杰. 抚顺西露天煤矿地质灾害预测. 自然灾害学报,2006,15(4):132-137 CSCD被引 4 次    
16.  王现国. 小秦岭矿区地质灾害发育特征与易发性分区. 地下水,2010,32(4):162-164 CSCD被引 1 次    
17.  张国丽. 基于GIS与BP神经网络的采空塌陷易发性预测. 热带地理,2015,35(5):770-776 CSCD被引 2 次    
18.  Lee S. Spatial prediction of ground subsidence susceptibility using an Artificial Neural Network. Environmental Management,2012,49(2):347-358 CSCD被引 2 次    
19.  Suh J. GIS-based evaluation of mining-induced subsidence susceptibility considering 3D multiple mine drifts and estimated mined panels. Environmental Earth Sciences,2016,75(10):1-19 CSCD被引 1 次    
20.  Oh H J. Sensitivity analysis for the GIS-based mapping of the ground subsidence hazard near abandoned underground coal mines. Environmental Earth Sciences,2011,64(2):347-358 CSCD被引 4 次    
引证文献 5 篇

1 郭瑞 基于SBAS-InSAR的矿区采空区潜在滑坡综合识别方法 地球信息科学学报,2019,21(7):1109-1120
CSCD被引 11 次

2 杨灿灿 基于街景影像的城市道路空间舒适度研究 地球信息科学学报,2021,23(5):785-801
CSCD被引 5 次

显示所有5篇文献

论文科学数据集
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