| 基于灰色关联度模型的区域滑坡敏感性评价 |
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| 引用本文： | 黄发明,汪洋,董志良,吴礼舟,郭子正,张泰丽.基于灰色关联度模型的区域滑坡敏感性评价[J].地球科学,2019,44(2):664-676. |
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| 作者姓名： | 黄发明 汪洋 董志良 吴礼舟 郭子正 张泰丽 |
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| 作者单位： | 1.南昌大学建筑工程学院, 江西南昌 330000 |
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| 基金项目： | 国家自然科学基金项目41807285国家自然科学基金项目41572289国家自然科学基金项目41572292中国地质调查局项目（浙江飞云江流域地质灾害调查）DD201602082 |
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| 摘 要： | 数理统计和机器学习模型如支持向量机(support vector machine,SVM)等,在区域滑坡敏感性评价中得到广泛的应用.但这些模型的建模过程往往较复杂,如在对机器学习进行训练和测试时难以选取合理的非滑坡栅格单元,而且有较多的模型参数需要确定.为提高滑坡敏感性评价建模的效率和精度,提出基于灰色关联度的敏感性评价模型.灰色关联度模型能有效计算各比较样本与参考样本之间的定量的关联度,具有建模过程简洁和评价精度高的优点,该模型目前在区域滑坡敏感性评价中的应用还没有引起研究人员的足够关注且有待进一步拓展.拟将灰色关联度模型用于浙江省飞云江流域南田—雅梅图幅(南田地区)的滑坡敏感性评价,并将得到的评价结果与SVM模型的敏感性评价结果作对比分析.结果显示,灰色关联度模型在高和极高敏感区的滑坡预测精度优于SVM模型,而在中等敏感区的滑坡预测精度略低于SVM模型;整体而言,灰色关联度模型对整个南田地区滑坡敏感性分布的预测精度略高于SVM模型.对两个模型建模过程的对比结果显示,灰色关联度模型建模较简单,具有比SVM模型更高的建模效率,为滑坡敏感性评价提供了一种新思路.
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| 关 键 词： | 滑坡 敏感性评价 灰色关联度 支持向量机 |
| 收稿时间： | 2018-12-20 |
Regional Landslide Susceptibility Mapping Based on Grey Relational Degree Model |
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| Huang Faming,Wang Yang,Dong Zhiliang,Wu Lizhou,Guo Zizheng,Zhang Taili.Regional Landslide Susceptibility Mapping Based on Grey Relational Degree Model[J].Earth Science-Journal of China University of Geosciences,2019,44(2):664-676. |
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| Authors: | Huang Faming Wang Yang Dong Zhiliang Wu Lizhou Guo Zizheng Zhang Taili |
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| Institution: | (School of Civil Engineering and Architecture,Nanchang University,Nanchang 330000,China;Faculty of Engineering China University of Geosciences,Wuhan 430074,China;College of Environmental and Civil Engineering,Chengdou University of Technology Chengdu 610059,China;Nanjing Center,China Geological Survey,Nanjing 210016,China) |
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| Abstract: | Statistical and machine learning models, such as support vector machine (SVM), have been widely used to assess the landslide susceptibility. However, the modeling processes of statistical and machine learning model are generally complex.For example, it is difficult to select reasonable non-landslide grid cells when the machine learning models are trained and tested, and many model parameters need to be determined.In order to improve the efficiency and accuracy of the model used for landslide susceptibility assessment, the grey relational degree (GRD) model is proposed. The GRD model can efficiently calculate the quantitative relational degrees between the comparative samples and the reference sample, and it has the advantages of simple modeling process and accurate assessment results.However, few studies have been done on the GRD model.In this study, the GRD model is used to assess the landslide susceptibility in the Nantian and Yamei maps (Nantian area) in the Feiyunjiang River basin, Zhejiang Province of China, and the assessment results of the GRD model are compared with those of the SVM model. The results show that the GRD model has higher prediction rate than the SVM model in the high and very high susceptibility areas, and has slightly lower prediction rate than the SVM in the moderate susceptibility area. On the whole, the GRD model has slightly higher prediction rate than the SVM for landslide susceptibility assessment in Nantian area. Meanwhile, the results also show that the model process of GRD is simple, it has higher efficiency than the SVM. The GRD model provides a novel idea for landslide susceptibility assessment. |
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| Keywords: | landslide susceptibility assessment grey relational degree support vector machine |
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