| 基于不同斜坡单元划分方法和BP神经网络的泥石流易发性评价 |
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| 引用本文： | 李坤,赵俊三,林伊琳,周豹.基于不同斜坡单元划分方法和BP神经网络的泥石流易发性评价[J].测绘通报,2022,0(8):68-74. |
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| 作者姓名： | 李坤 赵俊三 林伊琳 周豹 |
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| 作者单位： | 1. 昆明理工大学国土资源工程学院, 云南 昆明 650093;2. 智慧矿山地理空间信息集成创新重点实验室, 云南 昆明 650093;3. 云南省高校自然资源空间信息集成与应用科技创新团队, 云南 昆明 650211 |
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| 基金项目： | 国家自然科学基金(41761081);昆明理工大学引进人才科研启动基金(KKZ3202021055) |
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| 摘 要： | 选取合适的评价单元是泥石流易发性评价的关键,为探索斜坡单元不同划分方法对泥石流易发性评价结果的影响,本文以泥石流多发地东川区为例,对比分析了水文分析法和曲率分水岭法两种斜坡单元划分方法在泥石流易发性评价中的效果。首先在解译泥石流点的基础上,采用不同划分方法的斜坡单元作为评价单元,然后对初步选取的指标因子进行多重共线性和贡献率分析,以完善指标因子体系,最后构建基于BP神经网络的泥石流易发性评价模型。结果表明,泥石流极高和高易发区主要集中分布于研究区小江河谷和金沙江南岸,该地区地质环境脆弱,危险性较高。基于曲率分水岭法的易发性模型AUC值为0.865 8,高于水文分析法的0.815 3,表明采用曲率分水岭法划分的斜坡单元更适用于研究区泥石流易发性评价。
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| 关 键 词： | 泥石流 易发性评价 斜坡单元划分 BP神经网络 指标因子 |
| 收稿时间： | 2021-11-01 |
Assessment of debris flow susceptibility based on different slope unit division methods and BP neural network |
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| LI Kun,ZHAO Junsan,LIN Yilin,ZHOU Bao.Assessment of debris flow susceptibility based on different slope unit division methods and BP neural network[J].Bulletin of Surveying and Mapping,2022,0(8):68-74. |
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| Authors: | LI Kun ZHAO Junsan LIN Yilin ZHOU Bao |
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| Institution: | 1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China;2. Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China;3. Spatial Information Integration Technology of Natural Resources in Universities of Yunnan Province, Kunming 650211, China |
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| Abstract: | Selecting appropriate assessment units is the key to the assessment of debris flow susceptibility. In order to explore the impact of different methods of slope unit division on the assessment results of debris flow susceptibility. Taking Dongchuan district as an example, this paper compares and analyzes the effects of two slope unit division methods: hydrological analysis method and curvature watershed method in the evaluation of debris flow susceptibility. Based on the interpretation of debris flow points, the slope units with different division methods are used as the assessment unit, and the preliminary selected index factors are analyzed for multicollinearity and contribution rate to improve the index factor system, and finally build the debris flow susceptibility evaluation model based on BP neural network. The results show that the very high and high susceptibility areas of debris flow are mainly distributed in the Xiaojiang river valley and the south bank of Jinsha river in the study area, where the geological environment is fragile and the risk is high; The AUC value of the susceptibility model based on the curvature watershed method is 0.8658, which is higher than that of the hydrological analysis method of 0.8153, indicating that the slope unit divided by the curvature watershed method is more suitable for debris flow susceptibility assessment in the study area. |
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| Keywords: | debris flow susceptibility assessment slope unit division BP neural network index factor |
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