GIS支持下三峡库区秭归县滑坡灾害空间预测
收稿日期: 2010-02-05
修回日期: 2010-05-13
网络出版日期: 2010-10-20
基金资助
国家自然科学基金项目(40672205);国家高技术研究发展计划(2007AA12Z100);国土资源部三峡库区三期地质灾害防治重大科学研究项目(SXKY3-2-2)
Landslide hazard spatial prediction in Zigui County ofthe Three Gorges Reservoir Area based on GIS
Received date: 2010-02-05
Revised date: 2010-05-13
Online published: 2010-10-20
基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。
关键词： 滑坡; 空间预测; Logistic回归; 地理信息系统; 三峡库区
彭令, 牛瑞卿, 陈丽霞 . GIS支持下三峡库区秭归县滑坡灾害空间预测[J]. 地理研究, 2010 , 29(10) : 1889 -1898 . DOI: 10.11821/yj2010100017
Landslide prediction is very important in disaster prevention and reduction procedures, and it is one of practical research fields to evaluate and predict landslide hazards using statistic analysis model and spatial analysis of GIS. The aim of this study is to analyze landslide susceptibility using Logistic regression model in Zigui County of the Three Gorges Reservoir Area. In this paper, seven evaluation factors are selected, i.e. topographic slope, topographic aspect, bed rock-slope relationship, lithology, land use and distance from road and drainage. In susceptibility mapping, the use of logistic regression is to find the best fitting function to describe the relationship between the presence or absence of landslides (dependent variable) and a set of evaluation factors such as topographic slope and lithology. Here, an inventory map concerning 37 landslides was used to produce a variable, which takes a value of 1 for the presence and 0 for the absence of slope failures. In order to improve the accuracy and credibility of the model prediction, methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. An optimal sampling scheme that can eliminate spatial autocorrelation whilst maintaining enough samples to achieve the accuracy based on the model is developed. The model was tested by the overall model statistics, and the results indicate that the model fits the dataset. The effect of each parameter on landslide occurrence was assessed from the corresponding coefficient that appears in the logistic regression function. The interpretation of the coefficients showed that land use plays a major role in determining landslide occurrence and distribution, although field observations showed that engineering construction exerts great influence on slope stability. With the help of a predicted probability map, the study area was classified into four categories of landslide susceptibility: high, moderate, low and none. The moderate and high susceptibility zones make up 38.9% of the total study area. In comparison to the occurrence of historical landslide hazards, the precision using logistic regression model can be up to 77.57%.
Key words： landslide; spatial prediction; logistic regression; GIS; Three Gorges Reservoir Area
[1] 陈剑,杨志法,刘衢秋.滑坡的易滑度分区及概率预报模式.岩石力学与工程学报,2005,24(13):2392~2396.
[2] 戴福初,李军.暴雨滑坡泥石流的研究进展与趋向.地理研究,1998 ,17(增):117~124.
[3] 吴益平,殷坤龙,陈丽霞. 滑坡空间预测数学模型的对比及其应用.地质科技情报,2007 ,26(6):95~100.
[4] Barredol J,Benavidesz A,Herhl J,et al. Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. International Journal of Applied Earth Observation and Geoinformation,2000,2 :9~23.
[5] Ayalew L,Yamagishi H,Ugawa N. Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides,2004,1:73~81.
[6] Soeters R,Westen C J. Slope instability recognition, analysis, and zonation. In: Turner K A, Schuster R L. Landslides: Investigation and mitigation. Washington, USA: Transport Research Board,1996.129~177.
[7] Guzzetti F,Carrara A,Cardinali M,et al. Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, central Italy. Geomorphology,1999 ,31,181~216.
[8] Aleotti P,Chowdhury R. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment,1999 ,58:21~44.
[9] Carrara A,Crosta G,Frattini P. Geomorphological and historical data in assessing landslide hazard. Earth Surface Processes and Landforms,2003,28:1125~1142.
[10] Lee S,Ryu J H,Min K,et al . Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms,2003 ,28:1361~1376.
[11] Dai F C,Lee C F. Landslide characteristics and slope instability modeling using GIS Lantau Island, Hong Kong. Geomorphology ,2002,42:213~238.
[12] 陈丽霞,殷坤龙,张桂荣.浙江省磐安县滑坡灾害空间预测.地球与环境,2005 ,33(增刊):359~363.
[13] 李雪平,唐辉明,陈实.基于GIS的Logistic回归在区域滑坡空间预测中的应用.公路交通科技 ,2005,22(6):152~155.
[14] Bai S B,Wang Jian, Lu G N, et al.GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology,2010 ,115:23~31.
[15] Lee S,Min K. Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology,2001,40:1095~1113.
[16] Ohlmacher C G,Davis C J. Using multiple regression and GIS technology to predict landslide hazard in northeast Kansas,USA. Engineering Geology ,2003,69:331~343.
[17] Lulseged Ayalew,Hiromitsu Yamagishi. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology ,2005,65:15~31.
[18] 王济川,郭志刚. Logistic回归模型——方法与应用.北京:高等教育出版社,2001.
[19] 胡立山,冷坚,王锡璠,等.1∶5万新滩(西)、过河口(西)幅区域地质调查报告.湖北:湖北省地质矿产局,1995.
[20] 兰恒星,王苓涓,周成虎.地理信息系统支持下的滑坡灾害分析模型研究.工程地质学报 ,2002,10 (4): 421~427.
[21] 李军, 周成虎. 基于栅格GIS滑坡风险评价方法中格网大小选取分析. 遥感学报,2003 ,7 (2): 86~92.
[22] Bo Huang, Li Zhang, Bo Wu. Spatiotemporal analysis of rural-urban land conversion. International Jorunal of Geographical Information Science,2009 ,23(3):379~398.
[23] Getis A,Griffith D A. Comparative spatial filtering in regression analysis. Geographical Analysis,2002,34(2):130~140.
[24] Yesilnacar E, Topal T.Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology,2005,79:251~266.
[25] Menard S. Applied logistic regression analysis. Sage University Paper Series on Quantitative applications in Social Sciences, vol. 106. California: Sage Publications,1995.98.
[26] Clark W A,Hosking P L. Statistical Methods for Geographers. New York: John Wiley and Sons ,1986.518.
[27] 胡德勇,赵文吉,李小娟,等.不完备样本条件下基于支持向量回归模型的滑坡易发性评价.地理研究,2008 ,27(4):755~762.
[28] Chung C F,Fabbri A G. Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Sensing,1999,65(12):1388~1399.
/
| 〈 |
|
〉 |