• 中国出版政府奖提名奖

    中国百强科技报刊

    湖北出版政府奖

    中国高校百佳科技期刊

    中国最美期刊

    Volume 28 Issue 5
    Sep.  2003
    Turn off MathJax
    Article Contents
    HU Cheng, CHEN Zhi-hua, CHEN Xue-jun, 2003. ANN- and GIS-Based Regional Prediction of Cover-Collapse Probability: A Case Study in West Part of Guilin City. Earth Science, 28(5): 557-562.
    Citation: HU Cheng, CHEN Zhi-hua, CHEN Xue-jun, 2003. ANN- and GIS-Based Regional Prediction of Cover-Collapse Probability: A Case Study in West Part of Guilin City. Earth Science, 28(5): 557-562.

    ANN- and GIS-Based Regional Prediction of Cover-Collapse Probability: A Case Study in West Part of Guilin City

    • Received Date: 2003-05-19
    • Publish Date: 2003-09-25
    • Cover-collapse is one of frequent geological hazards in karst zone. Predicting the probability of cover-collapse occurrence is a requisite task for territorial planning, resource exploiting and hazard harnessing. Regional quantitative prediction of cover-collapse probability has become very intricate problem for the following reasons: (1)interactions between many influencing factors; (2)a sophisticated developing process; (3) difficulty associated with the value acquisition of factors. Some recent prediction models can not display the nonlinear characteristicsof collapse development pattern, nor can they eliminate the impact of empiricism during the course of weights allocation. Three major characteristics of artifical neural network(ANN)technology, i.e. self-learning, self-adapting and nonlinear mapping, indicate a powerful application potential in collapse prediction field. This paper reports the methodology of developing an ANN model to predict cover-collapse occurrence probability. An approach was established to measure the relative probability of the collapse corresponding to certain factor combinations, and some impact factors were specified. Consequently, the structure of ANN prediction model was created.292 stochastic collapse samples from the sample aggregate, of which the size was 312, were used to train the ANN model. The testing results of the 20 remaining samples show that this model has a good precision. Evaluation grids division and their value acquisition of every factor were accomplished with the aid of GIS software tools. The unstable probability of each grid was calculated through the trained ANN model, which enables us to delineate the different stable zones in the study area.

       

    • loading
    • [1]
      Goodings D J, Abdulla W A. Stability charts for predicting sinkholes in weakly cemented sand over karst limestone [J]. Engineering Geology, 2002, 65: 179-184. doi: 10.1016/S0013-7952(01)00127-2
      [2]
      陈星, 黄润秋. 岩溶塌陷的地质概化模型[J]. 水文地质与工程地质, 2002, (6): 30-34. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG200206009.htm

      CHEN X, HUANG R Q. Geological conceptive models of karst collapse[J]. Hydrogeology and Engineering Geology, 2002, (6): 30-34. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG200206009.htm
      [3]
      张发旺, 贾秀梅, 赵华. 灰色统计方法及其在岩溶塌陷预测分析中的应用[J]. 河北地质学院学报, 1996, 19(2): 144-150. https://www.cnki.com.cn/Article/CJFDTOTAL-HBDX602.005.htm

      ZHANG F W, JIA X M, ZHAO H. Application of gray system statistic method in the field of karst collapse prediction analysis[J]. Journal of Hebei College of Geology, 1996, 19(2): 144-150. https://www.cnki.com.cn/Article/CJFDTOTAL-HBDX602.005.htm
      [4]
      陈学军, 陈植华, 陈先华, 等. 桂林西城区岩溶塌陷模糊层次综合评价[J]. 桂林工学院学报, 2000, 20(1): 112-116. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGX200002003.htm

      CHEN X J, CHEN Z H, CHEN X H, et al. Fuzzy-hierarchy prediction of karst collapse in the west area of Guilin city[J]. Journal of Guilin Institute of Technology, 2000, 20(1): 112-116. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGX200002003.htm
      [5]
      Kaufmann O, Quinif Y. Geohazard map of cover-collapse sinkholes in the" Torunaisis" area, southern Belgium[J]. Engineering Geology, 2002, 65: 117-124. doi: 10.1016/S0013-7952(01)00118-1
      [6]
      贺玉龙, 杨立中, 黄涛. 人工神经网络在岩溶塌陷预测中的应用研究[J]. 中国地质灾害与防治学报, 1999, 10 (4): 86-90. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH199904013.htm

      HE Y L, YANG L Z, HUANG T. Research on application of artificial neural network in the prediction of karst collapse[J]. Chinese Journal of Geological Hazard and Control, 1999, 10(4): 86-90. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH199904013.htm
      [7]
      包惠明, 胡长顺. 岩溶地面塌陷神经网络预测[J]. 工程地质学报, 2002, 10(3): 299-304. doi: 10.3969/j.issn.1004-9665.2002.03.014

      BAO H M, HU C S. Neural network prediction of karstic ground collapse[J]. Journal of Engineering Geology, 2002, 10(3): 299-304. doi: 10.3969/j.issn.1004-9665.2002.03.014
      [8]
      陈守余, 周梅春. 人工神经网络模拟实现与应用[M]. 武汉: 中国地质大学出版社, 2000. 1-36.

      CHEN S Y, ZHOU M C. Realization and application of ANN models[M]. Wuhan: China University of Geosciences Press, 2000. 1-36.
      [9]
      阎平凡, 张长水. 人工神经网络与模拟进化计算[M]. 北京: 清华大学出版社, 2000. 1-32.

      YAN P F, ZHANG C S. ANN and simulating calculations of evolution[M]. Beijing: Tsinghua University Press, 2000. 1-32.
      [10]
      朱寿赠, 周健红, 陈学军. 桂林市西城区岩溶塌陷形成条件及主要影响因素[J]. 桂林工学院学报, 2000, 20 (2): 100-105. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGX200002001.htm

      ZHU S Z, ZHOU J H, CHEN X J. Analysis of forming conditions and main influential factors of karst collapse in west urban district, Guilin city[J]. Journal of Guilin In-stitute of Technology, 2000, 20(2): 100-105. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGX200002001.htm
      [11]
      Chen X J, Chen Z H, Hu C. The dominant factors and spatial-temporal distribution of karst collapse in the west area of Guilin city[A]. Proceedings of international symposium on hydrogeology and the environment[C]. Beijing: China Environmental Science Press, 2000. 502-507.
      [12]
      Cooley T. Geological and geotechnical context of cover collapse and subsidence in mid-continent US clay-mantled karst[J]. Environmental Geology, 2002, 42: 469-475.
    • 加载中

    Catalog

      通讯作者: 陈斌, bchen63@163.com
      • 1. 

        沈阳化工大学材料科学与工程学院 沈阳 110142

      1. 本站搜索
      2. 百度学术搜索
      3. 万方数据库搜索
      4. CNKI搜索

      Figures(3)  / Tables(3)

      Article views (3948) PDF downloads(19) Cited by()
      Proportional views

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return