地理科学 ›› 2014, Vol. 34 ›› Issue (1): 110-115.doi: 10.13249/j.cnki.sgs.2014.01.110

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基于三种不同模型的区域滑坡灾害敏感性评价及结果检验研究

邱海军1(), 曹明明1, 刘闻1, 王彦民2, 郝俊卿3, 胡胜1   

  1. 1.西北大学城市与环境学院, 陕西 西安 710127
    2.陕西理工学院化学与环境科学学院,陕西 汉中 723001
    3.西安财经学院商学院, 陕西 西安 710061
  • 收稿日期:2013-03-29 修回日期:2013-06-10 出版日期:2014-01-10 发布日期:2013-08-16
  • 作者简介:

    作者简介:邱海军(1983-),男,陕西神木人,博士,主要研究方向为灾害和土地利用研究。E-mail:rgbitxpl@163.com

  • 基金资助:
    西北大学科学研究基金(12NW32)、西北大学科研启动基金(PR12076)、陕西省社会科学界2012年度重大理论与现实问题研究项目(2012Z029)共同资助

The Susceptibility Assessment of Landslide and Its Calibration of the Models Based on Three Different Models

Hai-jun QIU1(), Ming-ming CAO1, Wen LIU1, Yan-min WANG2, Jun-qing HAO3, Sheng HU1   

  1. 1.College of Urban and Environment Science, Northwest University, xi’an, Shaanxi 710127, China
    2.School of Chemistry and Environmental Science, Shaanxi University of Technology, Hanzhong, Shaanxi 723001,China
  • Received:2013-03-29 Revised:2013-06-10 Online:2014-01-10 Published:2013-08-16

摘要:

选取相对高差、坡度、坡向、水系、距断层距离、植被覆盖、地层岩性和道路等影响因子,采用信息量法、Logistic回归和人工神经网络3种模型进行滑坡灾害的敏感性评价,并对评价结果进行检验。结果表明:① 评价分类结果的准确性会关系到社会经济成本。经过采用Cohen’s Kappa系数法、Sridevi Jadi精度评估方法和ROC曲线3种方法对评价结果进行比较分析,结果显示人工神经网络模型具有更好的评价精度。② 宁强县滑坡地域分布上,呈现一带三区。其中高、中和低敏感区分别占全县总面积的39.96%,37.7%和22.33%。

关键词: 滑坡, 敏感性评价, 结果检验

Abstract:

Landslide disaster restricts the sustainable development of human beings which would cause deaths and injuries, property damage and living environment ruins seriously. The regions should be divided into deferent types on the base of disaster risk when making macroeconomic policy of regional geological disaster. Thus, it is very necessary to make susceptibility assessment on zoning prone and risk of geological disasters in these regions firstly. When different assessment models are employed, the results are different. Furthermore, land types according to result of the susceptibility would results in difference in economy. Thus, it was more important to employ suitable model whose susceptibility assessment results were objective and realistic to the fact; however, there were few reports in this field in China yet. This study made assessments on the susceptibility of landslide disaster and evaluated the results. The employed susceptibility assessment models were information value, logistic regression and artificial neural network model. The relative relief, slope, aspect, river system, distance to fault, vegetation cover, formation lithology and road were chosen as factors. The results were showed as following. Firstly, the accuracy of classification influenced the social economic cost. Cohen’s Kappa factor method, precision evaluation method proposed by Sridevie Jadi and ROC curve method as the evaluation methods were used to evaluate the assessment results obtained from above models. The Kappa coefficients were 0.72, 0.69 and 0.55 by artificial neural network model, logistic regression method and information value model, respectively. The empirical probity (namely accuracy of prediction results) proposed by Sridevie Jadi of above 3 models was 87.48%, 74.26% and 69.54%, respectively. The AUC values were 0.805, 0.724 and 0.684, respectively. These evaluations proved that the assessment result obtained by artificial neural network model was more realistic to the fact. As a result, artificial neural network model performed the highest level of accuracy in the 3 models. Secondly, there could be one zone and 3 areas according to the landslide assessment results in Ningqiang County. They were: the zone of two sides of Mianxian County-Yang pingguan-Jin shanshi fault, volcanic area of Da′an-Miaoba-Gongjiahe-Daijiaba, shale, siltstone and slate area of Tiesuoguan-Hujiaba, phyllite, slate and sandstone area of Anlehe-Guangping, respectively. The area of high-susceptibility area was 1 300.85 km2 which accounted for 39.96% of the county area. Landslide in this area was well developed which was affected by Jinshansi-Yangpingguan-Mianxian fault obviously. The area of medium and low susceptibility was 1 227.34 km2 and 727.02 km2 which accounted for 37.7% and 22.33% respectively.

Key words: landslide, susceptibility assessment, calibration of the models

中图分类号: 

  • P954