Susceptibility evaluation of valley debris flow based on dual-channel network with fusion attention mechanism
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摘要:
针对泥石流灾害评估问题，文章提出了一种新的轻量化卷积神经网络模型——融合注意力机制的双通道网络（dual-channel fusion attention mechanism network，DCFAMNet），旨在快速识别沟谷型泥石流灾害。首先，根据历史泥石流点记录，以沟谷数字高程图像（digital elevation map，DEM）及遥感影像为数据源，设计以双通道网络结构为基础技术框架，在DEM图像特征提取通道引入通道注意力机制强调图像特征的网络通道权重，在遥感影像特征通道引入3D卷积块提取沟谷的地表信息，在特征融合阶段利用深度可分离卷积进行更多的特征信息交互。其次，对相关流域的潜在威胁沟谷作出易发性预测，绘制泥石流灾害易发性图。最后，可视化DCFAMNet提取到的沟谷坡向、曲率、坡度等深层特征定位目标关键特征。结果表明，利用DCFAMNet结合GIS技术对泥石流沟谷的识别率可达到80%，AUC值为0.75，表现良好。保存模型最佳参数评估相关沟谷易发性，通过ArcGIS做可视化分析将泥石流灾害分为5个评价等级，并确定泥石流极高易发性，得出高易发区主要分布在贡山县独龙江干流、福贡县怒江干流等水系区域，兰坪县相对较安全。结果可为山区泥石流防灾减灾工作提供有用的参考和依据。
Abstract:In addressing the issue of debris flow disaster assessment, this paper proposes a novel lightweight convolutional neural network model, the Dual-Channel Fusion Attention Mechanism Network (DCFAMNet), designed to rapidly identifying the susceptibility of gully-type debris flows. The main contributions of this paper are as follows: Firstly, based on historical debris flow records and using Digital Elevation Maps (DEMs) and remote sensing images as data sources, a dual-channel network structure is designed as the basic technical framework. Within the DEM image feature extraction channel, a channel attention mechanism is introduced to emphasize the channel weights of the image features, while in the remote sensing image feature extraction channel, 3D convolutional blocks are employed to extract the surface information of the gullies. In the feature fusion stage, depthwise separable convolutions are used to facilitate more interaction of feature information. Secondly, the susceptibility prediction of potential threats gullies in the related basins is made, and susceptibility maps of debris flow disasters are generated. Finally, DCFAMNet visualizes the extracted deep features such as gully slope, curvature, and slope orientation. Experimental results indicate that, by integrating the DCFAMNet with GIS technology, the identification rate for debris flow gullies can reach up to 80%, with an AUC value of 0.75, indicating good performance. The best parameters of the model are retained for assessing the susceptbility scores of the relevant gullies. Through visualization analysis in ArcGIS, the debris flow disaster risk is categorized into five assessment levels. It is determined that the extremely high susceptibility and high susceptibility zones for debris flows are primarily distributed in the mainstream of the Dulong River in Gongshan County and the mainstream of the Nujiang River in Fugong County, while Lanping County is relatively safe. The findings of this research can provide valuable insights and foundations for the prevention and mitigation of debris flow disasters in mountainous regions.
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表 1 样本分类
Table 1. Sample classification
所属类别 正样本 负样本 类别 0 1 2 3 4 5 流域面积/km2 (1, 24] (26, 64] (69, 109] (1, 12] (12, 27] (30, 45] 数据增强/个 45 51 44 48 46 54 
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表 2 正负2分类测试混淆矩阵
Table 2. Confusion matrix for two-class testing (Positive, negative)
预测值 真实值 正 负 正 110 20 负 37 113
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表 3 6分类测试混淆矩阵
Table 3. Confusion matrix for 6-category testing
预测值 真实值 0 1 2 3 4 5 0 27 1 0 10 7 1 1 0 23 1 0 5 14 2 0 0 58 0 0 0 3 5 0 0 38 3 0 4 5 0 0 0 29 2 5 10 0 0 0 2 39
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表 4 试验结果
Table 4. Summary of experimental results
数据（90∶10） 数据（80∶20） 数据（70∶30） Test2-acc Test6-acc Test2-acc Test6-acc Test2-acc Test6-acc DCFAMNet 80%±4% 76%±4% 68%±5% 64%±5% 62%±5% 60%±5% ResNet18[20] 76%±3% 70%±3% 64%±3% 64%±3% 57%±5% 57%±5% ResNet34 78%±5% 71%±3% 69%±a>% 65%±5% 60%±5% 58%±5% ShuffleNet[21] 72%±6% 70%±6% 60%±8% 56%±8% 55%±6% 51%±6% SENet[22] 78%±4% 65%±4% 62%±4% 54%±4% 54%±5% 48%±5% 注：DCFAMNet为轻量型卷积神经网络—融合注意力机制的双通道网络（Dual-Channel Fusion Attention Mechanism Network），ResNet指网络模型Residual Network，ShuffleNet指网络模型ShufleNet Volution，SENet指网络模型Squeeze-and-Excitation Network。
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表 5 模型性能
Table 5. Summary of model performance
Precision-2 Recall-2 F1-score-2 Kappa-2 Precision-6 Recall-6 DCFAMNet 0.75 0.85 0.79 0.59 0.75 0.75 ResNet18 0.68 0.79 0.73 0.43 0.66 0.68 ResNet34 0.68 0.85 0.78 0.58 0.68 0.74 ShuffleNet 0.80 0.69 0.79 0.45 0.78 0.65 SENet 0.69 0.83 0.78 0.56 0.65 0.73
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表 6 消融试验结果
Table 6. Pertubation experiment results
precision recall F1-score kappa Test2-acc Test6-acc basic Net 0.66 0.50 0.59 0.40 65%±5% 60%±5% with ECA 0.75 0.81 0.74 0.50 75%±4% 70%±4% with 3DCNN 0.66 0.55 0.60 0.45 72%±5% 68%±5% with DepSep 0.72 0.71 0.75 0.48 71%±3% 71%±3% DCFAMNet 0.75 0.85 0.79 0.59 80%±4% 76%±4% 注：basic Net为基础网络模型，ECA表示Efficient Channel Attention，3DCNN为3D卷积，DepSep为深度卷积，DCFAMNet为轻量型卷积神经网络—融合注意力机制的双通道网络（Dual-Channel Fusion Attention Mechanism Network）。
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表 7 注意力模型对比结果
Table 7. Comparison results of attention models
注意力模型 Test2-acc Test6-acc basic Net 75%±4% 71%±4% With SE 76%±3% 72%±3% With CBAM 78%±5% 75%±5% With ECA 80%±4% 76%±4% 注：basic Net为基础网络模型，SE表示Squeeze-and-Excitation，CBAM表示Convolutional Block Attention Module，ECA表示Efficient Channel Attention。
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表 8 地貌条件、物源条件
Table 8. Geomorphic conditions and provenance conditions
地貌条件和物源条件 练登大沟 腊早村 石缸河 主沟长度/km 10.300 7.824 24.342 面积/km2 16.260 16.450 87.174 高程差/km 1.386 2.426 2.590 坡降比 0.130 0.310 0.106 平均坡度/（°） 16.170 21.200 12.160 Melton指数 0.340 0.598 0.277 土壤条件 不饱和雏形土、简育高活性淋溶土 高活性淋溶土 高活性淋溶土、铁质低活性强酸土、腐殖质低活性强酸土、
简育高活性强酸土、饱和雏形土地层岩性 板岩、千枚岩、杂砂岩、长石砂岩、沙岩、
石灰岩和其他碳酸盐岩片麻岩、板岩、千枚岩、
片岩、花岗岩花岗岩、玄武岩、片麻岩、板岩、千枚岩、砂岩、杂砂岩、
长石砂岩、页岩、石灰石、其他碳酸盐岩植被条件[26] 疏林地、高覆盖度草地、其它建设用地 有林地、灌木林、疏林地、
高覆盖度草地水田、旱地、有林地、疏林地、高覆盖度草地、中覆盖度草地
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[1] 刘文,王猛,朱赛楠,等. 基于光学遥感技术的高山极高山区高位地质灾害链式特征分析——以金沙江上游典型堵江滑坡为例[J]. 中国地质灾害与防治学报,2021,32(5):29 − 39. [LIU Wen,WANG Meng,ZHU Sainan,et al. An analysis on chain characteristics of highstand geological disasters in high mountains and extremely high mountains based on optical remote sensing technology:A case study of representative large landslides in upper reach of Jinsha River[J]. The Chinese Journal of Geological Hazard and Control,2021,32(5):29 − 39. (in Chinese with English abstract)]
LIU Wen, WANG Meng, ZHU Sainan, et al. An analysis on chain characteristics of highstand geological disasters in high mountains and extremely high mountains based on optical remote sensing technology: A case study of representative large landslides in upper reach of Jinsha River［J］. The Chinese Journal of Geological Hazard and Control, 2021, 32（5）: 29 − 39. （in Chinese with English abstract）
[2] 时岩,张金霞,刘兴荣,等. 陇南市武都区滑坡转化为泥石流灾害链形成机理及防治措施研究[J]. 水利规划与设计,2022(10):136 − 139. [SHI Yan,ZHANG Jinxia,LIU Xingrong,et al. Study on the formation mechanism of landslide-induced mudslide disaster chain and prevention and control measures in Wudou District,Longnan City[J]. Water Resources Planning and Design,2022(10):136 − 139. (in Chinese)] doi: 10.3969/j.issn.1672-2469.2022.10.027
SHI Yan, ZHANG Jinxia, LIU Xingrong, et al. Study on the formation mechanism of landslide-induced mudslide disaster chain and prevention and control measures in Wudou District, Longnan City［J］. Water Resources Planning and Design, 2022（10）: 136 − 139. （in Chinese） doi: 10.3969/j.issn.1672-2469.2022.10.027
[3] 郑琅,张欣,王立娟. 四川省甘洛县山体滑坡应急调查与成因机制分析[J]. 人民长江,2022,53(8):117 − 122. [ZHENG Lang,ZHANG Xin,WANG Lijuan. Emergency investigation and formation mechanism of landslide in Ganluo County,Sichuan Province[J]. Yangtze River,2022,53(8):117 − 122. (in Chinese with English abstract)]
ZHENG Lang, ZHANG Xin, WANG Lijuan. Emergency investigation and formation mechanism of landslide in Ganluo County, Sichuan Province［J］. Yangtze River, 2022, 53（8）: 117 − 122. （in Chinese with English abstract）
[4] CARRARA A,MERENDA L. Landslide inventory in northern Calabria,southern Italy[J]. Geological Society of America Bulletin,1976,87(8):1153. doi: 10.1130/0016-7606(1976)87<1153:LIINCS>2.0.CO;2
[5] ZHOU Wei,TANG Chuan,VAN ASCH T W J,et al. A rapid method to identify the potential of debris flow development induced by rainfall in the catchments of the Wenchuan earthquake area[J]. Landslides,2016,13(5):1243 − 1259. doi: 10.1007/s10346-015-0631-0
[6] 侯圣山,曹鹏,陈亮,等. 基于数值模拟的耳阳河流域泥石流灾害危险性评价[J]. 水文地质工程地质,2021,48(2):143 − 151. [HOU Shengshan,CAO Peng,CHEN Liang,etal. Risk assessment of debris flow disasters in Eryang River basin based on numerical simulation[J]. Hydrogeology & Engineering Geology,2021,48(2):143 − 151. (in Chinese with English abstract)]
HOU Shengshan, CAO Peng, CHEN Liang, etal. Risk assessment of debris flow disasters in Eryang River basin based on numerical simulation［J］. Hydrogeology & Engineering Geology, 2021, 48（2）: 143 − 151. （in Chinese with English abstract）
[7] 乔渊,刘铁骥,陈亮,等. 基于Massflow模型的甘肃省岷县二马沟泥石流危险性评价[J]. 水利水电技术,2020,51(4):184 − 192. [QIAO Yuan,LIU Tieji,CHEN Liang,et al. Massflow model-based hazard assessment on Erma gully debris flow in Minxian County of Gansu Province[J]. Water Resources and Hydropower Engineering,2020,51(4):184 − 192. (in Chinese with English abstract)]
QIAO Yuan, LIU Tieji, CHEN Liang, et al. Massflow model-based hazard assessment on Erma gully debris flow in Minxian County of Gansu Province［J］. Water Resources and Hydropower Engineering, 2020, 51（4）: 184 − 192. （in Chinese with English abstract）
[8] ROWBOTHAM D,DE SCALLY F,LOUIS J. The identification of debris torrent basins using morphometric measures derived within a GIS[J]. Geografiska Annaler:Series A,Physical Geography,2005,87(4):527-537.
[9] 丛威青,潘懋,李铁锋,等. 降雨型泥石流临界雨量定量分析[J]. 岩石力学与工程学报,2006,25(增刊1):2808 − 2812. [CONG Weiqing,PAN Mao,LI Tiefeng,et al. Quantitative analysis of critical rainfall of rainfall debris flow[J]. Chinese Journal of Rock Mechanics and Engineering,2006,25(Sup1):2808 − 2812. (in Chinese with English abstract)]
CONG Weiqing, PAN Mao, LI Tiefeng, et al. Quantitative analysis of critical rainfall of rainfall debris flow［J］. Chinese Journal of Rock Mechanics and Engineering, 2006, 25（Sup1）: 2808 − 2812. （in Chinese with English abstract）
[10] 王常明,田书文,王翊虹,等. 泥石流危险性评价:模糊c均值聚类-支持向量机法[J]. 吉林大学学报(地球科学版),2016,46(4):1168 − 1175. [WANG Changming,TIAN Shuwen,WANG Yihong,et al. Risk assessment of debris flow:A method of SVM based on FCM[J]. Journal of Jilin University (Earth Science Edition),2016,46(4):1168 − 1175. (in Chinese with English abstract)]
WANG Changming, TIAN Shuwen, WANG Yihong, et al. Risk assessment of debris flow: A method of SVM based on FCM［J］. Journal of Jilin University （Earth Science Edition）, 2016, 46（4）: 1168 − 1175. （in Chinese with English abstract）
[11] 张书豪,吴光. 随机森林与GIS的泥石流易发性及可靠性[J]. 地球科学,2019,44(9):3115 − 3134. [ZHANG Shuhao,WU Guang. Debris flow susceptibility and its reliability based on random forest and GIS[J]. Earth Science,2019,44(9):3115 − 3134. (in Chinese with English abstract)]
ZHANG Shuhao, WU Guang. Debris flow susceptibility and its reliability based on random forest and GIS［J］. Earth Science, 2019, 44（9）: 3115 − 3134. （in Chinese with English abstract）
[12] 孔艳,王保云,王乃强,等. 滇西高山峡谷区泥石流危险性评价——以怒江傈僳族自治州为例[J]. 云南师范大学学报(自然科学版),2019,39(3):63 − 70. [KONG Yan,WANG Baoyun,WANG Naiqiang,et al. Debris flows risk assessment in the alpine canyon of western Yunnan:A case study in Nujiang Lisu autonomous prefecture[J]. Journal of Yunnan Normal University (Natural Sciences Edition),2019,39(3):63 − 70. (in Chinese with English abstract)]
KONG Yan, WANG Baoyun, WANG Naiqiang, et al. Debris flows risk assessment in the alpine canyon of western Yunnan: A case study in Nujiang Lisu autonomous prefecture［J］. Journal of Yunnan Normal University （Natural Sciences Edition）, 2019, 39（3）: 63 − 70. （in Chinese with English abstract）
[13] 钱闪光,李云,侯克鹏,等. 怒江州贡山县从尼泥石流形成条件与诱发机理[J]. 四川建材,2018,44(12):94 − − 95. [QIAN Shanguang,LI Yun,HOU Kepeng,et al. Formation conditions and induced mechanism of debris flow in Congni,Gongshan County,Nujiang Prefecture[J]. Sichuan Building Materials,2018,44(12):94 − 95. (in Chinese)]
QIAN Shanguang, LI Yun, HOU Kepeng, et al. Formation conditions and induced mechanism of debris flow in Congni, Gongshan County, Nujiang Prefecture［J］. Sichuan Building Materials, 2018, 44（12）: 94 − 95. （in Chinese）
[14] 李益敏,杨蕾,魏苏杭. 基于小流域单元的怒江州泥石流易发性评价[J]. 长江流域资源与环境,2019,28(10):2419 − 2428. [LI Yimin,YANG Lei,WEI Suhang. Susceptibility assessment of debris flow in Nujiang befecture based on the catchment[J]. Resources and Environment in the Yangtze Basin,2019,28(10):2419 − 2428. (in Chinese with English abstract)]
LI Yimin, YANG Lei, WEI Suhang. Susceptibility assessment of debris flow in Nujiang befecture based on the catchment［J］. Resources and Environment in the Yangtze Basin, 2019, 28（10）: 2419 − 2428. （in Chinese with English abstract）
[15] STRAHLER A N. Quantitative analysis of watershed geomorphology[J]. Transactions,American Geophysical Union,1957,38(6):913. doi: 10.1029/TR038i006p00913
[16] 刘德玉,贾贵义,李松,等. 地形因素对白龙江流域甘肃段泥石流灾害的影响及权重分析[J]. 水文地质工程地质,2019,46(3):33 − 39. [LIU Deyu,JIA Guiyi,LI Song,et al. Impacts of topographical factors on debris flows and weight analysis at the Gansu segment of the Bailongjiang River Basin[J]. Hydrogeology & Engineering Geology,2019,46(3):33 − 39. (in Chinese with English abstract)] doi: 10.16030/j.cnki.issn.1000-3665.2019.03.05
LIU Deyu, JIA Guiyi, LI Song, et al. Impacts of topographical factors on debris flows and weight analysis at the Gansu segment of the Bailongjiang River Basin［J］. Hydrogeology & Engineering Geology, 2019, 46（3）: 33 − 39. （in Chinese with English abstract） doi: 10.16030/j.cnki.issn.1000-3665.2019.03.05
[17] 李彩侠,马煜,何元勋. 泥石流致灾因子敏感性分析——以四川都江堰龙溪河流域为例[J]. 中国地质灾害与防治学报,2020,31(5):32 − 39. [LI Caixia,MA Yu,HE Yuanxun. Sensitivity analysis of debris flow to environmental factors:A case of Longxi River Basin in Dujiangyan,Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2020,31(5):32 − 39. (in Chinese with English abstract)]
LI Caixia, MA Yu, HE Yuanxun. Sensitivity analysis of debris flow to environmental factors: A case of Longxi River Basin in Dujiangyan, Sichuan Province［J］. The Chinese Journal of Geological Hazard and Control, 2020, 31（5）: 32 − 39. （in Chinese with English abstract）
[18] WANG Qilong,WU Banggu,ZHU Pengfei,et al. ECA-net:efficient channel attention for deep convolutional neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13-19,2020,Seattle,WA,USA. IEEE,2020:11531 − 11539.
[19] HOWARD A G,ZHU Menglong,CHEN Bo,et al. MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL]. 2017:arXiv:1704.04861.
[20] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30,2016,Las Vegas,NV,USA. IEEE,2016:770 − 778.
[21] ZHANG Xiangyu,ZHOU Xinyu,LIN Mengxiao,et al. ShuffleNet:An extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23,2018,Salt Lake City,UT,USA. IEEE,2018:6848 − 6856.
[22] HU Jie,SHEN Li,SUN Gang. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23,2018,Salt Lake City,UT,USA. IEEE,2018:7132 − 7141.
[23] 张国平,徐晶,毕宝贵. 滑坡和泥石流灾害与环境因子的关系[J]. 应用生态学报,2009,20(3):653 − 658. [ZHANG Guoping,XU Jing,BI Baogui. Relations of landslide and debris flow hazards to environmental factors[J]. Chinese Journal of Applied Ecology,2009,20(3):653 − 658. (in Chinese with English abstract)]
ZHANG Guoping, XU Jing, BI Baogui. Relations of landslide and debris flow hazards to environmental factors［J］. Chinese Journal of Applied Ecology, 2009, 20（3）: 653 − 658. （in Chinese with English abstract）
[24] 张书豪,吴光,张乔,等. 基于子流域特征的泥石流易发性评价[J]. 水文地质工程地质,2018,45(2):142 − 149. [ZHANG Shuhao,WU Guang,ZHANG Qiao,et al. Debris-flow susceptibility assessment using the characteristic factors of a catchment[J]. Hydrogeology & Engineering Geology,2018,45(2):142 − 149. (in Chinese with English abstract)]
ZHANG Shuhao, WU Guang, ZHANG Qiao, et al. Debris-flow susceptibility assessment using the characteristic factors of a catchment［J］. Hydrogeology & Engineering Geology, 2018, 45（2）: 142 − 149. （in Chinese with English abstract）
[25] 李益敏,袁静,蒋德明,等. 基于GIS的西南高山峡谷区滑坡风险性评价——以怒江州泸水市为例[J]. 西北师范大学学报(自然科学版),2021,57(6):94 − 102. [LI Yimin,YUAN Jing,JIANG Deming,et al. Risk assessment of landslide disasters in alpine canyon area based on GIS:Taking Lushui City,Nujiang Prefecture as an example[J]. Journal of Northwest Normal University (Natural Science),2021,57(6):94 − 102. (in Chinese with English abstract)]
LI Yimin, YUAN Jing, JIANG Deming, et al. Risk assessment of landslide disasters in alpine canyon area based on GIS: Taking Lushui City, Nujiang Prefecture as an example［J］. Journal of Northwest Normal University （Natural Science）, 2021, 57（6）: 94 − 102. （in Chinese with English abstract）
[26] 中国科学院资源环境科学数据中心(http://www.resdc.cn/). (2019). 中国土地利用数据(1980-2015).国家青藏高原科学数据中心. [Resource and Environment Science Data Center of Chinese Academy of Sciences (http://www.resdc.cn/). (2019). China Land Use Data (1980-2015). National Qinghai-Tibet Plateau Scientific Data Center.]
Resource and Environment Science Data Center of Chinese Academy of Sciences （http://www.resdc.cn/）. （2019）. China Land Use Data （1980-2015）. National Qinghai-Tibet Plateau Scientific Data Center.
[27] 王欢,陈廷方,丁明涛. 泥石流对岩性的敏感性分析及其在危险性评价中的应用[J]. 长江流域资源与环境,2012,21(3):385 − 390. [WANG Huan,CHEN Tingfang,DING Mingtao. Sensitivity analysis to lithology and application in risk assessment of debris flow[J]. Resources and Environment in the Yangtze Basin,2012,21(3):385 − 390. (in Chinese with English abstract)]
WANG Huan, CHEN Tingfang, DING Mingtao. Sensitivity analysis to lithology and application in risk assessment of debris flow［J］. Resources and Environment in the Yangtze Basin, 2012, 21（3）: 385 − 390. （in Chinese with English abstract）
[28] 鲁科,余斌,韩林,等. 泥石流流域岩性的坚固系数与暴发频率的关系[J]. 地球科学进展,2011,26(9):980 − 990. [LU Ke,YU Bin,HAN Lin,et al. A study of the relationship between frequency of debris flow and the lithology in the catchment of debris flow[J]. Advances in Earth Science,2011,26(9):980 − 990. (in Chinese with English abstract)]
LU Ke, YU Bin, HAN Lin, et al. A study of the relationship between frequency of debris flow and the lithology in the catchment of debris flow［J］. Advances in Earth Science, 2011, 26（9）: 980 − 990. （in Chinese with English abstract）
[29] 何坤,胡卸文,刘波,等. 川藏铁路某车站泥石流群发育特征及对线路的影响[J]. 水文地质工程地质,2021,48(5):137 − 149. [HE Kun,HU Xiewen,LIU Bo,et al. Characteristics and potential engineering perniciousness of the debris flow group in one station of the Sichuan-Tibet Railway[J]. Hydrogeology & Engineering Geology,2021,48(5):137 − 149. (in Chinese with English abstract)]
HE Kun, HU Xiewen, LIU Bo, et al. Characteristics and potential engineering perniciousness of the debris flow group in one station of the Sichuan-Tibet Railway［J］. Hydrogeology & Engineering Geology, 2021, 48（5）: 137 − 149. （in Chinese with English abstract）
[30] 施蕾蕾,陈宁生,杨成林,等. 娃娃沟流域泥石流活动与植被关系探讨[J]. 水土保持研究,2008,15(3):96 − 99. [SHI Leilei,CHEN Ningsheng,YANG Chenglin,et al. Relationships between vegetation and debris flow in well-vegetated Wawagou valley[J]. Research of Soil and Water Conservation,2008,15(3):96 − 99. (in Chinese with English abstract)]
SHI Leilei, CHEN Ningsheng, YANG Chenglin, et al. Relationships between vegetation and debris flow in well-vegetated Wawagou valley［J］. Research of Soil and Water Conservation, 2008, 15（3）: 96 − 99. （in Chinese with English abstract）
[31] 戚国庆,黄润秋. 泥石流成因机理的非饱和土力学理论研究[J]. 中国地质灾害与防治学报,2003,14(3):12 − 15. [QI Guoqing,HUANG Runqiu. Study on genetic and mechanical analysis of debris flow based on unsaturated soils mechanics[J]. The Chinese Journal of Geological Hazard and Control,2003,14(3):12 − 15. (in Chinese with English abstract)]
QI Guoqing, HUANG Runqiu. Study on genetic and mechanical analysis of debris flow based on unsaturated soils mechanics［J］. The Chinese Journal of Geological Hazard and Control, 2003, 14（3）: 12 − 15. （in Chinese with English abstract）
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