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胡涛,樊鑫,王硕,等. 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, 2019, 19(35): 61-69.
HU Tao,et al.Collapse susceptibility assessment of Sinan county based on radial basis function neural network[J].Science Technology and Engineering,2019,19(35):61-69.
基于径向基神经网络的思南县崩塌易发性评价
Collapse susceptibility assessment of Sinan county based on radial basis function neural network
投稿时间:2019-03-30  修订日期:2019-08-29
DOI:
中文关键词:  崩塌  易发性评价  径向基神经网络  遥感  地理信息系统
英文关键词:collapse disaster  susceptibility assessment  radial basis function neural network  remote sensing  geographic information system
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
           
作者单位
胡涛 中国地质大学(武汉)
樊鑫 贵州地质工程勘查院
王硕 贵州省铜仁市国土资源局
冷信风 江西省九江市修水县国土资源局
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中文摘要:
      (目的) 贵州省思南县境内崩塌地质灾害较为发育,通过对县域崩塌易发性进行预测,可准确获取崩塌地质灾害分布规律,为国土部门开展崩塌防治提供科学指导。(方法和过程) 因此,本研究采用遥感和地理信息系统等技术,对思南县的自然地理和地质条件等因素进行分析;再采用频率比分析和相关性分析法,建立崩塌地质灾害与基础影响因子间的非线性响应关系;最后,首次提出一种典型的机器学习:径向基神经网络模型,对思南县崩塌易发性进行预测并绘制崩塌易发性分布图。(结论)结果表明:径向基神经网络模型高精度地预测出了崩塌地质灾害的分布规律,其预测思南县的崩塌易发性的准确率(AUC曲线)达到0.945。且崩塌易发性分布图显示, 极高、高、中等、低和极低易发区面积占比分别为13.06%、14.08%、25.41%、23.68%和23.77%。
英文摘要:
      The collapse geological disasters are widely distributed in the Sinan county of Guizhou province. As a result, the local economic development and the safety of people and properties in Sinan county are seriously threatened by the collapses. Hence, it is very necessary to in depth explore the development rules of collapse disasters. In recent years, the collapse susceptibility assessment (CSA) has been developed as an effective tool for collapse prediction and prevention, the development rules and distribution features of collapses in Sinan county can be revealed through CSA. Therefore, this study adapts the Remote Sensing (RS) and Geographic Information System (GIS) technologies, to analysis the physic-geographical environment and geological conditions of Sinan county; and then uses frequency ratio and correlation analysis methods to build the nonlinear dynamic corresponding relationship between the basic condition factors and collapse disasters. Finally, a typical machine learning model, radial basis function neural network (RBFNN) is firstly adapted to assess the collapse susceptibility and then map collapse susceptibility based on GIS. Results show that, the area under the receiver operating characteristic curve is 0.945, suggesting that the distribution rules of collapse disasters in Sinan county are predicted very accurately using the RBFNN model; meanwhile, the proportions of very high, high, moderate, low and very low susceptible areas in the whole area of Sinan county are respectively 13.06%、14.08%、25.41%、23.68% and 23.77%.
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