| 基于地貌信息熵的天山公路冰川泥石流危险性评价 |
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| 引用本文： | 谢涛,尹前锋,高贺,郭峰,林达明.基于地貌信息熵的天山公路冰川泥石流危险性评价[J].冰川冻土,2019,41(2):400-406. |
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| 作者姓名： | 谢涛 尹前锋 高贺 郭峰 林达明 |
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| 作者单位： | 中国地质大学(北京) 工程技术学院,北京,100083;中国科学院大学 地球与行星科学学院,北京,100049;交通运输部 公路科学研究院,北京,100083 |
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| 基金项目： | 交通运输部建设科技项目（2014318365110）资助 |
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| 摘 要： | 冰川泥石流广泛分布于我国西部地区，特殊的地貌条件对泥石流的发育有着重要影响。地貌信息熵理论是量化地貌发育阶段的指标，针对冰川地貌条件，探讨了地貌信息熵理论在冰川地貌条件下的应用，并提出了相应的修正系数。选取天山公路沿线13条泥石流沟，基于ArcGIS平台，进行了危险性评价。结果表明，修正后的地貌信息熵值能较好地判断冰川泥石流的危险程度。
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| 关 键 词： | 天山公路 冰川泥石流 地貌信息熵 危险性评价 |
| 收稿时间： | 2018-05-08 |
| 修稿时间： | 2018-08-01 |
Risk assessment of glacial debris flow along the Tianshan Highway based on geomorphic information entropy |
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| XIE Tao,YIN Qianfeng,GAO He,GUO Feng,LIN Daming.Risk assessment of glacial debris flow along the Tianshan Highway based on geomorphic information entropy[J].Journal of Glaciology and Geocryology,2019,41(2):400-406. |
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| Authors: | XIE Tao YIN Qianfeng GAO He GUO Feng LIN Daming |
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| Institution: | 1. School of Engineering and Technology, China University of Geosciences(Beijing), Beijing 100083, China;2. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;3. Research Institute of Highway Ministry of Transport, Beijing 100083, China |
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| Abstract: | Glacial debris flow is widely distributed in western China, where the special landform conditions have significantly impacted the development of glacial debris flow hazards. Geomorphic information entropy theory is applied to quantify the development stages of geomorphology. In this paper, the application of geomorphic information entropy theory to glacial geomorphic condition is discussed and the corresponding correction coefficients under various glacial geomorphic conditions are proposed. Thirteen debris flow gullies along the Tianshan Highway were selected for risk assessment based on the ArcGIS platform. As a result, it is found that the corrected geomorphic information entropy value is able to judge the risk of glacial debris flow. |
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| Keywords: | Tianshan Highway glacial debris flow geomorphic information entropy risk assessment |
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