Volume 28 Issue 2
Apr.  2022
Turn off MathJax
Article Contents
LI Xin, XUE Guicheng, LIU Changzhu, et al., 2022. Evaluation of geohazard susceptibility based on information value model and information value-logistic regression model: A case study of the central mountainous area of Hainan Island. Journal of Geomechanics, 28 (2): 294-305. DOI: 10.12090/j.issn.1006-6616.2021111
Citation: LI Xin, XUE Guicheng, LIU Changzhu, et al., 2022. Evaluation of geohazard susceptibility based on information value model and information value-logistic regression model: A case study of the central mountainous area of Hainan Island. Journal of Geomechanics, 28 (2): 294-305. DOI: 10.12090/j.issn.1006-6616.2021111

Evaluation of geohazard susceptibility based on information value model and information value-logistic regression model: A case study of the central mountainous area of Hainan Island

doi: 10.12090/j.issn.1006-6616.2021111
Funds:

the Hainan Provincial Natural Science Foundation of China 421RC664

the Hainan Provincial Natural Science Foundation of China 2019RC347

More Information
  • Received: 2021-08-26
  • Revised: 2021-11-30
  • As the basis of geohazard risk evaluation, the geohazard susceptibility evaluation can objectively and accurately reflect the probability of geological hazard occurrence by using quantitative mathematical statistics. This article takes Wuzhishan city, where occurs the most geohazards in Hainan Island, as an example. Factors including structure, rock formations, slopes, topographic undulations, altitude variation coefficients, normalized differential vegetation index (NDVI), rainfall, river systems, roads, and curvatures were selected as evaluation indicators and applied in both information value model and information value-logistic regression model. In the end, by comparing and analyzing the accuracy and adaptability of both models, the article ends with the conclusion that the high-prone areas are mainly distributed along roads and rivers in the mountainous areas, and the extremely low-prone areas are mainly located in the areas where have no rivers, valleys and human activities. In addition, the results also revealed that the prediction accuracy meets the requirements of susceptibility evaluation owing to the high AUC (area under the curve) values occupying 0.897 and 0.896 respectively in both models. Evaluation factors such as rainfall, elevation variation coefficient and highway play a remarkable role on the development of geohazards. Furthermore, it is indicated by experiments that the information value-logistic regression model has better prediction accuracy than the other. The research results provide a scientific and effective discrimination method and a prediction approach for geohazard risk evaluation in this area.

     

  • loading
  • CHEN Y, XUE G C, LIU C Z, et al., 2017. Zoning of probable occurrence levels of geological hazards in Baoting county, Hainan Province[J]. Geology and Resources, 26(2): 165-170. (in Chinese with English abstract)
    CHENG X J, YANG W M, XIANG L Z, et al., 2017. Risk assessment of seismic loess landslide based on Newmark Model in Beishan, Tianshui City[J]. Journal of Geomechanics, 23(2): 296-305. (in Chinese with English abstract)
    DONG L J, 2017. Evaluation of ecosystem service value and its driving force analysis based on landscape pattern: taking Chengdu plain and Longmen mountain transition zone as an example[D]. Chengdu: Sichuan Normal University. (in Chinese with English abstract)
    DU G L, ZHANG Y S, GAO J C, et al., 2016. Landslide susceptibility assessment based on GIS in Bailongjiang Watershed, Gansu Province[J]. Journal of Geomechanics, 22(1): 1-11. (in Chinese with English abstract)
    DU X C, 2020. Hazard assessment of landslide in Dechang based on ArcGIS and SPSS[D]. Mianyang: Southwest University of Science and Technology. (in Chinese with English abstract)
    GUZZETTI F, REICHENBACH P, CARDINALI M, et al., 2005. Probabilistic landslide hazard assessment at the basin scale[J]. Geomorphology, 72(1-4): 272-299. doi: 10.1016/j.geomorph.2005.06.002
    HE B F, ZHANG J G, 2012. Assessment of geo-hazards Ludila Hydropower susceptibility in reservoir of Station, Jinsha River[J]. The Chinese Journal of Geological Hazard and Control, 23(2): 75-82. (in Chinese with English abstract)
    HU T, FAN X, WANG S, et al., 2019. Collapse susceptibility assessment of Sinan county based on radial basis function neural network[J]. Science Technology and Engineering, 19(35): 61-69. (in Chinese with English abstract)
    LI W, YANG C B, 2009. Research on causes of geological disaster on Mesozoic clastic rock in Huangshan area[J]. Engineering and Construction, 23(4): 524-526. (in Chinese with English abstract)
    LI X, 2015. Evaluation of geological hazard zonation based on ArcGIS and logistic regression[D]. Xi'an: Chang'an University. (in Chinese with English abstract)
    LI X, RUAN M, YANG F, et al., 2022. Evaluation of geological hazard susceptibility based on GIS and information method: a case study of Changjiang, Hainan Province[J]. Geology and Resources, 31(1): 98-105. (in Chinese with English abstract)
    LI Y Y, MEI H B, REN X J, et al., 2018. Geological disaster susceptibility evaluation based on certainty factor and support vector machine[J]. Journal of Geo-information Science, 20(12): 1699-1709. (in Chinese with English abstract)
    LIANG L P, LIU Y G, TANG Z G, et al., 2019. Geologic hazards susceptibility assessment based on weighted information value: a case study in Luding county, Sichuan Province[J]. Bulletin of Soil and Water Conservation, 39(6): 176-182. (in Chinese with English abstract)
    LUO L G, PEI X J, HUANG R Q, et al., 2021. Landslide susceptibility assessment in Jiuzhaigou scenic area with GIS based on certainty factor and logistic regression model[J]. Journal of Engineering Geology, 29(2): 526-535. (in Chinese with English abstract)
    LUO Y H, ZHANG L, ZHANG Y C, 1998. Geological disaster risk assessment method. Beijing: Geological Press: 32-58. (in Chinese)
    MIAO X, TANG M G, WANG Z G, et al., 2016. Comparative analysis and application of model for assessment on risk of geological hazard[J]. Water Resources and Hydropower Engineering, 47(4): 119-122. (in Chinese with English abstract)
    QI X, HUANG B L, LIU G N, et al., 2017. Landslide susceptibility assessment in the Three Gorges Area, China, Zigui Synclinal Basin, using GIS technology and frequency ratio model[J]. Journal of Geomechanics, 23(1): 97-104. (in Chinese with English abstract)
    QIN Y G, YANG G L, JIANG X Y, et al., 2020. Geohazard susceptibility assessment based on integrated certainty factor model and logistic regression model for Kaiyang, China[J]. Science Technology and Engineering, 20(1): 96-103. (in Chinese with English abstract)
    SINGH P, SHARMA A, SUR U, et al., 2021. Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India[J]. Environment, Development and Sustainability, 23(4): 5233-5250. doi: 10.1007/s10668-020-00811-0
    TIAN C S, LIU X L, WANG J, 2016. Geohazard susceptibility assessment based on CF model and Logistic Regression models in Guangdong[J]. Hydrogeology and Engineering Geology, 43(6): 154-161, 170. (in Chinese with English abstract)
    WAN J W, FENG C J, QI B S, et al., 2020. Characteristics and susceptibility evaluation of geohazard development in Shunping county, Hebei Province[J]. Journal of Geomechanics, 26(4): 604-614. (in Chinese with English abstract)
    XIA N, LI X, YANG F, et al., 2015. 1∶50, 000 detailed survey report of geological disasters in Wuzhishan County[R]. Haikou: Hainan Geological Survey Institute. (in Chinese)
    XU C, DAI F C, YAO X, et al., 2010. GIS based certainty factor analysis of landslide triggering factors in Wenchuan earthquake[J]. Chinese Journal of Rock Mechanics and Engineering, 29(S1): 2972-2981. (in Chinese with English abstract)
    XU Y Z, LU Y N, LI D Y, et al., 2016. GIS and information model based landslide susceptibility assessment in granite area of Guangxi Province[J]. Journal of Engineering Geology, 24(4): 693-703. (in Chinese with English abstract)
    YANG G, XU P H, CAO C, et al., 2019. Assessment of regional landslide susceptibility based on combined model of certainty factor method[J]. Journal of Engineering Geology, 27(5): 1153-1163. (in Chinese with English abstract)
    YANG H Y, XU X N, YANG H F, 2020. The Jiuzhaigou co-seismic landslide hazard assessment based on weight of evidence method[J]. The Chinese Journal of Geological Hazard and Control, 31(3): 20-29. (in Chinese with English abstract)
    YE T J, XIE Q, WANG Y, 2019. Stability investigation and treatment evaluation of slopes in the eastern Tibet section of the Sichuan-Tibet Highway[J]. Journal of Geomechanics, 25(2): 233-239.
    ZANG L P, SHAN Y X, KONG X, 2018. Application of GIS-based analytic hierarchy process in geological hazards assessment in Tongde County[J]. Journal of Qinghai University, 36(6): 58-64. (in Chinese with English abstract)
    ZHANG F, ZHAO Z G, XIE D W, et al., 2019. Comparative study on landslide sensitivity assessment in Turks county of Xinjiang based on determined coefficient method and information quantity model[J]. Xinjiang Geology, 37(4): 575-579. (in Chinese with English abstract)
    ZHANG X D, LIU X N, ZHAO Z P, et al., 2018. Comparative study of geological hazards susceptibility assessment: constraints from the information value + logistic regression model and the CF + logistic regression model[J]. Geoscience, 32(3): 602-610. (in Chinese with English abstract)
    ZHAO D L, LANCUO Z M, HOU G L, et al., 2021. Assessment of geological disaster susceptibility in the Hehuang valley of Qinghai Province[J]. Journal of Geomechrics, 27(1): 83-95. (in Chinese with English abstract)
    ZHU H C, CHEN N, LIU H Y, et al., 2005. Research on the relief based on 1∶10000 DEMs: a case study in the loess plateau of north Shaanxi Province[J]. Science of Surveying and Mapping, 30(4): 86-88. (in Chinese with English abstract)
    ZHU J H, 2014. Study on the relationship between slope geometrical morphology and landslide collapse disasters in Yan'an[D]. Xi'an: Chang'an University. (in Chinese with English abstract)
    陈毅, 薛桂澄, 柳长柱, 等, 2017. 海南省保亭县地质灾害易发程度区划[J]. 地质与资源, 26(2): 165-170. doi: 10.3969/j.issn.1671-1947.2017.02.011
    程小杰, 杨为民, 向灵芝, 等, 2017. 基于Newmark模型的天水市北山地震黄土滑坡危险性评价[J]. 地质力学学报, 23(2): 296-305. doi: 10.3969/j.issn.1006-6616.2017.02.013
    董丽君, 2017. 基于景观格局的山地平原过渡带生态系统服务价值评估及其驱动力研究: 以成都平原与龙门山过渡带为例[D]. 成都: 四川师范大学.
    杜国梁, 张永双, 高金川, 等, 2016. 基于GIS的白龙江流域甘肃段滑坡易发性评价[J]. 地质力学学报, 22(1): 1-11. doi: 10.3969/j.issn.1006-6616.2016.01.001
    杜晓晨, 2020. 基于ArcGIS和SPSS的德昌县滑坡危险性评价研究[D]. 绵阳: 西南科技大学.
    何宝夫, 张加桂, 2012. 金沙江鲁地拉电站水库区地质灾害易发性评价[J]. 中国地质灾害与防治学报, 23(2): 75-82. doi: 10.3969/j.issn.1003-8035.2012.02.016
    胡涛, 樊鑫, 王硕, 等, 2019. 基于径向基神经网络的思南县崩塌易发性评价[J]. 科学技术与工程, 19(35): 61-69. doi: 10.3969/j.issn.1671-1815.2019.35.009
    李伟, 杨成斌, 2009. 黄山地区中生代碎屑岩区域地质灾害成因的探究[J]. 工程与建设, 23(4): 524-526. doi: 10.3969/j.issn.1673-5781.2009.04.030
    李翔, 2015. 以绥德县为例基于ArcGIS及逻辑回归法的地质灾害危险性评价[D]. 西安: 长安大学.
    李信, 阮明, 杨峰, 等, 2022. 基于GIS技术和信息量法的地质灾害易发性研究——以海南省昌江县为例[J]. 地质与资源, 31(1): 98-105. https://www.cnki.com.cn/Article/CJFDTOTAL-GJSD202201012.htm
    李远远, 梅红波, 任晓杰, 等, 2018. 基于确定性系数和支持向量机的地质灾害易发性评价[J]. 地球信息科学学报, 20(12): 1699-1709. doi: 10.12082/dqxxkx.2018.180349
    梁丽萍, 刘延国, 唐自豪, 等, 2019. 基于加权信息量的地质灾害易发性评价: 以四川省泸定县为例[J]. 水土保持通报, 39(6): 176-182. https://www.cnki.com.cn/Article/CJFDTOTAL-STTB201906028.htm
    罗路广, 裴向军, 黄润秋, 等, 2021. GIS支持下CF与Logistic回归模型耦合的九寨沟景区滑坡易发性评价[J]. 工程地质学报, 29(2): 526-535. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202102022.htm
    罗元华, 张梁, 张业成, 1998. 地质灾害风险评估方法. 北京: 地质出版社: 32-58.
    缪信, 汤明高, 王自高, 等, 2016. 地质灾害危险性评价模型的比较分析与应用[J]. 水利水电技术, 47(4): 119-122. https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ201604028.htm
    齐信, 黄波林, 刘广宁, 等, 2017. 基于GIS技术和频率比模型的三峡地区秭归向斜盆地滑坡敏感性评价[J]. 地质力学学报, 23(1): 97-104. doi: 10.3969/j.issn.1006-6616.2017.01.005
    覃乙根, 杨根兰, 江兴元, 等, 2020. 基于确定性系数模型与逻辑回归模型耦合的地质灾害易发性评价: 以贵州省开阳县为例[J]. 科学技术与工程, 20(1): 96-103. doi: 10.3969/j.issn.1671-1815.2020.01.015
    田春山, 刘希林, 汪佳, 2016. 基于CF和Logistic回归模型的广东省地质灾害易发性评价[J]. 水文地质工程地质, 43(6): 154-161, 170. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201606025.htm
    万佳威, 丰成君, 戚帮申, 等, 2020. 河北省顺平县地质灾害发育特征及易发性评价[J]. 地质力学学报, 26(4): 604-614. doi: 10.12090/j.issn.1006-6616.2020.26.04.053
    夏南, 李信, 杨峰, 等, 2015. 海南省五指山市1∶5万地质灾害详细调查报告[R]. 海口: 海南省地质调查院.
    许冲, 戴福初, 姚鑫, 等, 2010. 基于GIS的汶川地震滑坡灾害影响因子确定性系数分析[J]. 岩石力学与工程学报, 29(S1): 2972-2981. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2010S1057.htm
    许英姿, 卢玉南, 李东阳, 等, 2016. 基于GIS和信息量模型的广西花岗岩分布区滑坡易发性评价[J]. 工程地质学报, 24(4): 693-703. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201604031.htm
    杨光, 徐佩华, 曹琛, 等, 2019. 基于确定性系数组合模型的区域滑坡敏感性评价[J]. 工程地质学报, 27(5): 1153-1163. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201905027.htm
    杨华阳, 许向宁, 杨鸿发, 2020. 基于证据权法的九寨沟地震滑坡危险性评价[J]. 中国地质灾害与防治学报, 31(3): 20-29. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202003005.htm
    叶唐进, 谢强, 王鹰, 2019. 川藏公路藏东段边坡稳定性研究与治理评价[J]. 地质力学学报, 25(2): 233-239. doi: 10.12090/j.issn.1006-6616.2019.25.02.022
    臧丽萍, 山永祥, 孔逊, 2018. 基于GIS的层次分析法在同德县地质灾害易发性评价中的应用[J]. 青海大学学报, 36(6): 58-64. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXZ201806010.htm
    张峰, 赵忠国, 谢大伟, 等, 2019. 基于确定系数法和信息量模型对新疆特克斯县滑坡敏感性评价的对比研究[J]. 新疆地质, 37(4): 575-579. doi: 10.3969/j.issn.1000-8845.2019.04.024
    张晓东, 刘湘南, 赵志鹏, 等, 2018. 信息量模型、确定性系数模型与逻辑回归模型组合评价地质灾害敏感性的对比研究[J]. 现代地质, 32(3): 602-610. https://www.cnki.com.cn/Article/CJFDTOTAL-XDDZ201803018.htm
    赵东亮, 兰措卓玛, 侯光良, 等, 2021. 青海省河湟谷地地质灾害易发性评价[J]. 地质力学学报, 27(1): 83-95. doi: 10.12090/j.issn.1006-6616.2021.27.01.009
    朱红春, 陈楠, 刘海英, 等, 2005. 自1∶10000比例尺DEM提取地形起伏度: 以陕北黄土高原的实验为例[J]. 测绘科学, 30(4): 86-88. https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD200504028.htm
    祝俊华, 2014. 延安市斜坡几何形态与滑坡、崩塌相关性研究[D]. 西安: 长安大学.
  • 加载中

Catalog

    Figures(7)  / Tables(7)

    Article Metrics

    Article views (408) PDF downloads(66) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return