| 基于样本与因子优化的黄冈南部地区地质灾害易发性评价 |
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| 引用本文： | 陈前,晏鄂川,黄少平,王茜.基于样本与因子优化的黄冈南部地区地质灾害易发性评价[J].地质科技通报,2020,39(2):175-185. |
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| 作者姓名： | 陈前 晏鄂川 黄少平 王茜 |
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| 作者单位： | 中国地质大学（武汉）工程学院 |
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| 基金项目： | 国家自然科学基金项目41672313国家自然科学青年基金项目41807264 |
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| 摘 要： | 为对比信息量模型中灾害数量和灾害面积2种样本的适用性,以黄冈南部地区作为研究对象,探讨了评价因子的优化组合形式,采用信息量模型,根据研究区工程地质条件和地质灾害的特征初选评价因子,结合成功率曲线确定2种样本的因子优化组合,进而通过灾害比率及典型地质灾害点验证易发性评价结果。结果表明:①在单因子评价结果中,2种样本的单因子评价结果的AUC值排列顺序不尽相同,但呈现出一定规律性;②各叠加因子评价结果的准确度均达到因子优化组合的94.9%以上,变化幅度相对较小,且呈现出随因子数量增加而增大的趋势,但并不是越多越好;③2种样本的易发性评价结果都显示出高、较高易发区主要集中于研究区中部及北部地区,低易发区和较低易发区多集中于长江沿岸以及研究区南部,与灾害分布位置相符;④2种样本均为地质灾害易发性评价中信息量模型的有效计算样本,且面积样本的准确度明显优于数量样本。
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| 关 键 词： | 样本类别 因子优化 信息量模型 地质灾害 易发性评价 |
| 收稿时间： | 2019-05-28 |
Susceptibility evaluation of geological disasters in southern Huanggang based on samples and factor optimization |
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| Chen Qian,Yan Echuan,Huang Shaoping,Wang Qian.Susceptibility evaluation of geological disasters in southern Huanggang based on samples and factor optimization[J].Bulletin of Geological Science and Technology,2020,39(2):175-185. |
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| Authors: | Chen Qian Yan Echuan Huang Shaoping Wang Qian |
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| Affiliation: | (Faculty of Engineering,China University of Geosciences(Wuhan),Wuhan 430074,China) |
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| Abstract: | Taking southern Huanggang as the study area, this paper contrasted the applicability of the two kinds of calculation samples of the number of disasters and the disaster acreage in the information model, and explored the optimization combination of the evaluation factors. The study established the information model and selected the primary evaluation factors according to the engineering geological conditions and the characteristics of geological disasters of the study area. Also, the paper determined the optimization combination of factors with the success rate curve to verify the susceptibility evaluation results by disaster ratio and typical geological disaster. The results show that: ① In the single factor evaluation results, the order of the two AUC values is different but regular. ② The accuracy of each superposition factor evaluation result is above 94.9% of the optimal combination of factors, and the variation range is relatively small. This shows a trend of increase with the increase of the number of factors, but not as much as possible. ③ The results of the two calculation samples show that the high-prone areas are mainly concentrated in the central and northern parts of the study area, and that the low-prone areas are concentrated along the Yangtze River and in the southern part of the study area, consistent with the location of the disaster. ④ Both are the effective calculation samples of information value model in the geological disaster susceptibility evaluation, and the accuracy of the acreage sample is significantly better than that of the quantity sample. |
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| Keywords: | sample category factor optimization information value model geological disaster susceptibility evaluation |
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