| 基于信息量模型和信息量-逻辑回归模型的海南岛中部山区地质灾害易发性研究 |
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| 引用本文： | 李信,薛桂澄,柳长柱,夏南,杨永鹏,杨峰,王晓林,常振宇.基于信息量模型和信息量-逻辑回归模型的海南岛中部山区地质灾害易发性研究[J].地质力学学报,2022,28(2):294-305. |
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| 作者姓名： | 李信 薛桂澄 柳长柱 夏南 杨永鹏 杨峰 王晓林 常振宇 |
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| 作者单位： | 1.海南省海洋地质资源与环境重点实验室，海南 海口 570206 |
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| 基金项目： | 海南省自然科学基金（421RC664,2019RC347） |
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| 摘 要： | 地质灾害易发性评价作为地质灾害风险评价的基础，运用定量化的数学统计原理对地质灾害易发性进行研究能够客观准确地反映地质灾害发生的概率。文章以海南岛地质灾害最为发育的五指山市为例，选择断裂、岩土体、坡度、地形起伏度、海拔高程变异系数、归一化植被指数(NDVI)、降雨量、水系、公路、曲率值为评价指标，依托详查资料和遥感、地形数据，采用信息量模型和信息量-逻辑回归模型对研究区地质灾害易发性进行评价研究，评价结果经敏感性检验、频率比检验后表明:高易发区主要分布于山区公路和水系两侧沿线，极低易发区主要位于河谷不发育、人类工程活动较少的丘陵低山地带。两种模型的ROC曲线下面积值(AUC)分别为0.897和0.896，表明预测精度满足易发性评价要求。降雨、高程变异系数、公路等评价因子对地质灾害易发性起较强的控制作用。信息量-逻辑回归模型具有更高的可靠性和精准度，研究成果将为该地区地质灾害风险评价提供科学有效的判别方法和预测途径。
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| 关 键 词： | 信息量 逻辑回归 易发性 地质灾害 耦合模型 五指山市 |
| 收稿时间： | 2021/8/26 0:00:00 |
| 修稿时间： | 2021/11/30 0:00:00 |
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 |
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| LI Xin,XUE Guicheng,LIU Changzhu,XIA Nan,YANG Yongpeng,YANG Feng,WANG Xiaolin,CHANG Zhenyu.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[J].Journal of Geomechanics,2022,28(2):294-305. |
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| Authors: | LI Xin XUE Guicheng LIU Changzhu XIA Nan YANG Yongpeng YANG Feng WANG Xiaolin CHANG Zhenyu |
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| Institution: | 1.Hainan Provincial Key Laboratory of Marine Geological Resources and Environment, Haikou 570206, Hainan, China2.Hainan Geological Survey Institute, Haikou 570206, Hainan, China |
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| Abstract: | 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. |
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| Keywords: | information value logistic regression susceptibility geohazard coupled model Wuzhishan city |
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