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Article(id=1156907872505389158, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156907871645556837, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2402860, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1713456000000, receivedDateStr=2024-04-19, revisedDate=1721232000000, revisedDateStr=2024-07-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1753757931114, onlineDateStr=2025-07-29, pubDate=1737993600000, pubDateStr=2025-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753757931114, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753757931114, creator=13701087609, updateTime=1753757931114, updator=13701087609, issue=Issue{id=1156907871645556837, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='3', pageStart='879', pageEnd='1312', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753757930909, creator=13701087609, updateTime=1765095544280, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1204461268821320541, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156907871645556837, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1204461268825514846, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156907871645556837, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=933, endPage=941, ext={EN=ArticleExt(id=1156907872966762603, articleId=1156907872505389158, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Debris-flow Susceptibility Assessment in Yongsheng County Based on Gray Relational Analysis-information Volume Method, columnId=1156262729351549255, journalTitle=Science Technology and Engineering, columnName=Papers·Astronomy and Geosciences, runingTitle=null, highlight=null, articleAbstract=

In order to study the disaster susceptibility of debris flow in Yongsheng County, the research area was Yongsheng County of Lijiang City, Yunnan Province, and it was divided into 475 sub-watershed units. Grey correlation analysis method was used to calculate the correlation degree of each factor, and the factor with the lowest correlation degree was eliminated. The independence of factors was tested by collinearity diagnosis. In the end, eight factors including average slope, average annual maximum rainfall, average vegetation coverage, average elevation, average melton ratio, average water system density, average landslide core density and average road density were retained. The information volume of the factors was calculated by the information volume method, and the correlation degree value was taken as the weight value of the superposition of each factor. The grey correlation analysis-information volume model was further constructed to carry out the evaluation research on the vulnerability of debris flow in Yongsheng County. The results show this as follows. The requency ratio of debris flow disaster points in the extremely vulnerable area is as high as 4.06, and the area under the ROC (receiver operating characteristic) curve is 0.818, indicating that the selected eight factors and the grey correlation analysation-information volume method have good forecasting ability for the evaluation of debris flow disaster vulnerability. The results can also be used as reference for the prevention and control of debris flow disaster in Yongsheng County.

, correspAuthors=Zhi-fang ZHAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Kang-tai CHANG, Zhi-fang ZHAO, Qiao-mu MOU, Yong-lin YANG, Yun-fei HU, Yang QIN), CN=ArticleExt(id=1156907902561771696, articleId=1156907872505389158, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于灰色关联分析-信息量法的永胜县泥石流易发性评价, columnId=1156262730077163858, journalTitle=科学技术与工程, columnName=论文·天文学、地球科学, runingTitle=null, highlight=null, articleAbstract=

为研究永胜县泥石流灾害易发性,以云南省丽江市永胜县为研究区,将其划分为475个子流域单元。采用灰色关联分析法对各个评价因子进行关联度计算,剔除掉关联度最低的因子,使用共线性诊断进行独立性检验,最终保留流域平均坡度、流域多年平均最大降雨量、流域平均植被覆盖度、流域平均高程、流域平均melton比率、流域平均水系密度、流域平均崩滑核密度、流域平均道路密度等8个评价因子。评价因子的信息量通过信息量法计算得出,将关联度值作为各因子叠加的权重值,进一步构建灰色关联分析-信息量模型,开展永胜县泥石流易发性评价研究。结果表明:泥石流灾害极高易发区频率比高达4.06,接受者操作特征曲线(receiver operating characteristic curve, ROC)下的面积为0.818,说明选取的8个评价因子以及灰色关联分析-信息量评价模型对永胜县泥石流灾害易发性评价研究具有较好的预测能力,得到的评价结果也对当地的泥石流灾害防治工作提供了一定的参考作用。

, correspAuthors=赵志芳, authorNote=null, correspAuthorsNote=
* 赵志芳(1971—),女,白族,云南香格里拉人,博士,教授,博士研究生导师。研究方向:资源与环境遥感监测、矿化蚀变遥感异常增强提取、边境地区国土资源遥感监测等。E-mail:。
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常康泰(1999—),男,汉族,陕西西安人,硕士研究生。研究方向:InSAR沉降监测与神经网络。E-mail:。

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常康泰(1999—),男,汉族,陕西西安人,硕士研究生。研究方向:InSAR沉降监测与神经网络。E-mail:。

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常康泰(1999—),男,汉族,陕西西安人,硕士研究生。研究方向:InSAR沉降监测与神经网络。E-mail:。

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3. Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, MNR, Kunming 650051, China
4. Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, China
5. Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Kunming 650050, China
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3.自然资源部三江成矿作用及资源勘查利用重点实验室, 昆明 650051
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5.云南省国产高分卫星遥感地质工程研究中心, 昆明 650050
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Sources and types of data

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基础数据 数据来源 数据格式
DEM 地理空间数据云:http://www.gscloud.cn 30 m×30 m
栅格数据
崩塌、滑坡、泥石流统计清单、降雨数据 2021年度云南省重点区域地质灾害精细化调查与风险评价项目(永胜县) 矢量数据
Landsat8影像 美国地质调查局:https://landsatlook.usgs.gov 栅格数据
道路、断裂、水系 县域1∶5万DLG 矢量数据
), ArticleFig(id=1204542854929560535, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=CN, label=表1, caption=

数据来源和数据类型

, figureFileSmall=null, figureFileBig=null, tableContent=
基础数据 数据来源 数据格式
DEM 地理空间数据云:http://www.gscloud.cn 30 m×30 m
栅格数据
崩塌、滑坡、泥石流统计清单、降雨数据 2021年度云南省重点区域地质灾害精细化调查与风险评价项目(永胜县) 矢量数据
Landsat8影像 美国地质调查局:https://landsatlook.usgs.gov 栅格数据
道路、断裂、水系 县域1∶5万DLG 矢量数据
), ArticleFig(id=1204542855017640924, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=EN, label=Table 2, caption=

Correlation degree results of nine evaluation factors

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评价因子 关联度 排名
流域平均坡度 0.870 1
流域多年平均最大降雨量 0.854 2
流域平均植被覆盖度 0.854 3
流域平均高程 0.851 4
流域平均melton比率 0.849 5
流域平均水系密度 0.835 6
流域平均崩滑核密度 0.803 7
流域平均道路密度 0.800 8
流域平均断裂密度 0.763 9
), ArticleFig(id=1204542855130887139, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=CN, label=表2, caption=

9种评价因子关联度结果

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 关联度 排名
流域平均坡度 0.870 1
流域多年平均最大降雨量 0.854 2
流域平均植被覆盖度 0.854 3
流域平均高程 0.851 4
流域平均melton比率 0.849 5
流域平均水系密度 0.835 6
流域平均崩滑核密度 0.803 7
流域平均道路密度 0.800 8
流域平均断裂密度 0.763 9
), ArticleFig(id=1204542855235744747, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=EN, label=Table 3, caption=

Multicollinearity diagnosis results

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 共线性统计
容差 VIF
流域平均坡度 0.437 2.290
流域多年平均最大降雨量 0.461 2.171
流域平均植被覆盖度 0.705 1.419
流域平均高程 0.629 1.591
流域平均melton比率 0.490 2.042
流域平均水系密度 0.774 1.293
流域平均崩滑核密度 0.544 1.838
流域平均道路密度 0.775 1.291
), ArticleFig(id=1204542855336408053, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=CN, label=表3, caption=

多重共线性诊断结果

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 共线性统计
容差 VIF
流域平均坡度 0.437 2.290
流域多年平均最大降雨量 0.461 2.171
流域平均植被覆盖度 0.705 1.419
流域平均高程 0.629 1.591
流域平均melton比率 0.490 2.042
流域平均水系密度 0.774 1.293
流域平均崩滑核密度 0.544 1.838
流域平均道路密度 0.775 1.291
), ArticleFig(id=1204542855416099836, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=EN, label=Table 4, caption=

Evaluation information values of each factor

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 分级 泥石流
个数/个		 面积/km2 信息
量值
流域平均
坡度/(°)		 0~15 7 524.64 -1.004
15~20 43 1 059.07 0.109
20~25 66 1 735.58 0.044
25~30 49 1 057.17 0.241
>30 15 567.75 -0.321
流域多年平均
最大降雨量/mm		 0~650 36 1 031.64 -0.046
650~800 26 993.06 -0.333
800~900 54 1 742.88 -0.165
9 00~1 000 33 593.90 0.419
>1 000 31 566.06 0.405
流域平均
植被覆盖度		 0~0.3 1 124.77 -1.517
0.3~0.52 29 811.41 -0.022
0.52~0.65 46 1 658.08 -0.276
0.65~0.75 48 1 372.20 -0.044
0.75~1 56 958.82 0.469
流域平均
高程/m		 0~1 700 22 899.66 -0.398
1 700~2 000 68 1 061.96 0.565
2 000~2 300 38 1 234.52 -0.168
2 300~2 700 31 1 196.13 -0.340
>2 700 21 551.88 0.044
流域平均
melton比率		 0~0.14 3 372.14 -1.508
0.14~0.26 47 1 194.40 0.078
0.26~0.37 62 2 058.29 -0.189
0.37~0.54 45 875.90 0.344
>0.54 23 443.51 0.354
流域平均
水系密度/
(km·km-2)		 0~0.4 15 433.48 -0.051
0.4~0.54 44 1 538.60 -0.241
0.54~0.66 56 1 512.98 0.017
0.66~0.81 55 1 069.07 0.346
>0.81 10 390.09 -0.351
流域平均崩
滑核密度/
(个·km-2)		 0~0.04 55 1 659.49 -0.097
0.04~0.08 50 1 618.05 -0.167
0.08~0.12 33 932.16 -0.031
0.12~0.20 29 534.67 0.395
>0.20 13 183.45 0.663
流域平均
道路密度/
(km·km-2)		 0~0.4 31 1 400.99 -0.501
0.4~0.9 53 1 708.60 -0.163
0.9~1.5 52 1 003.75 0.349
1.5~2.2 28 508.50 0.410
>2.2 16 305.99 0.359
), ArticleFig(id=1204542855533539332, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=CN, label=表4, caption=

各评价因子信息量

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 分级 泥石流
个数/个		 面积/km2 信息
量值
流域平均
坡度/(°)		 0~15 7 524.64 -1.004
15~20 43 1 059.07 0.109
20~25 66 1 735.58 0.044
25~30 49 1 057.17 0.241
>30 15 567.75 -0.321
流域多年平均
最大降雨量/mm		 0~650 36 1 031.64 -0.046
650~800 26 993.06 -0.333
800~900 54 1 742.88 -0.165
9 00~1 000 33 593.90 0.419
>1 000 31 566.06 0.405
流域平均
植被覆盖度		 0~0.3 1 124.77 -1.517
0.3~0.52 29 811.41 -0.022
0.52~0.65 46 1 658.08 -0.276
0.65~0.75 48 1 372.20 -0.044
0.75~1 56 958.82 0.469
流域平均
高程/m		 0~1 700 22 899.66 -0.398
1 700~2 000 68 1 061.96 0.565
2 000~2 300 38 1 234.52 -0.168
2 300~2 700 31 1 196.13 -0.340
>2 700 21 551.88 0.044
流域平均
melton比率		 0~0.14 3 372.14 -1.508
0.14~0.26 47 1 194.40 0.078
0.26~0.37 62 2 058.29 -0.189
0.37~0.54 45 875.90 0.344
>0.54 23 443.51 0.354
流域平均
水系密度/
(km·km-2)		 0~0.4 15 433.48 -0.051
0.4~0.54 44 1 538.60 -0.241
0.54~0.66 56 1 512.98 0.017
0.66~0.81 55 1 069.07 0.346
>0.81 10 390.09 -0.351
流域平均崩
滑核密度/
(个·km-2)		 0~0.04 55 1 659.49 -0.097
0.04~0.08 50 1 618.05 -0.167
0.08~0.12 33 932.16 -0.031
0.12~0.20 29 534.67 0.395
>0.20 13 183.45 0.663
流域平均
道路密度/
(km·km-2)		 0~0.4 31 1 400.99 -0.501
0.4~0.9 53 1 708.60 -0.163
0.9~1.5 52 1 003.75 0.349
1.5~2.2 28 508.50 0.410
>2.2 16 305.99 0.359
), ArticleFig(id=1204542855642591242, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=EN, label=Table 5, caption=

Statistical table of debris flow susceptibility evaluation

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易发区
分级		 灾害
点/个		 区域分级
面积/
km2		 区域分级
面积占比
/%		 泥石流
分级面积
/km2		 泥石流
分级面积
占比/%		 频率比
低易发区 3 422.91 8.57 21.41 5.06 0.59
中易发区 37 1 957.30 39.64 501.92 25.64 0.65
高易发区 75 1 778.19 36.01 750.09 42.18 1.17
极高易发区 65 779.02 15.78 498.62 64.01 4.06
), ArticleFig(id=1204542855743254548, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156907872505389158, language=CN, label=表5, caption=

泥石流易发性评价统计表

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易发区
分级		 灾害
点/个		 区域分级
面积/
km2		 区域分级
面积占比
/%		 泥石流
分级面积
/km2		 泥石流
分级面积
占比/%		 频率比
低易发区 3 422.91 8.57 21.41 5.06 0.59
中易发区 37 1 957.30 39.64 501.92 25.64 0.65
高易发区 75 1 778.19 36.01 750.09 42.18 1.17
极高易发区 65 779.02 15.78 498.62 64.01 4.06
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基于灰色关联分析-信息量法的永胜县泥石流易发性评价
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常康泰 1 , 赵志芳 2, 3, 4, 5, 6, * , 牟乔木 1 , 杨永林 1 , 胡云飞 1 , 秦阳 1
科学技术与工程 | 论文·天文学、地球科学 2025,25(3): 933-941
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科学技术与工程 | 论文·天文学、地球科学 2025, 25(3): 933-941
基于灰色关联分析-信息量法的永胜县泥石流易发性评价
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常康泰1 , 赵志芳2, 3, 4, 5, 6, * , 牟乔木1, 杨永林1, 胡云飞1, 秦阳1
作者信息
  • 1.云南大学国际河流与生态安全研究院, 昆明 650050
  • 2.云南大学地球科学学院, 昆明 650050
  • 3.自然资源部三江成矿作用及资源勘查利用重点实验室, 昆明 650051
  • 4.云南省三江成矿与资源勘查利用重点实验室, 昆明 650051
  • 5.云南省国产高分卫星遥感地质工程研究中心, 昆明 650050
  • 6.云南省中老孟缅自然资源遥感监测国际联合实验室, 昆明 650051

    • 常康泰(1999—),男,汉族,陕西西安人,硕士研究生。研究方向:InSAR沉降监测与神经网络。E-mail:。

通讯作者:

* 赵志芳(1971—),女,白族,云南香格里拉人,博士,教授,博士研究生导师。研究方向:资源与环境遥感监测、矿化蚀变遥感异常增强提取、边境地区国土资源遥感监测等。E-mail:。
Debris-flow Susceptibility Assessment in Yongsheng County Based on Gray Relational Analysis-information Volume Method
Kang-tai CHANG1 , Zhi-fang ZHAO2, 3, 4, 5, 6, * , Qiao-mu MOU1, Yong-lin YANG1, Yun-fei HU1, Yang QIN1
Affiliations
  • 1. Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650050, China
  • 2. School of Earth Sciences, Yunnan University, Kunming 650050, China
  • 3. Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, MNR, Kunming 650051, China
  • 4. Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, China
  • 5. Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Kunming 650050, China
  • 6. Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650051, China
出版时间: 2025-01-28 doi: 10.12404/j.issn.1671-1815.2402860
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摘要
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为研究永胜县泥石流灾害易发性,以云南省丽江市永胜县为研究区,将其划分为475个子流域单元。采用灰色关联分析法对各个评价因子进行关联度计算,剔除掉关联度最低的因子,使用共线性诊断进行独立性检验,最终保留流域平均坡度、流域多年平均最大降雨量、流域平均植被覆盖度、流域平均高程、流域平均melton比率、流域平均水系密度、流域平均崩滑核密度、流域平均道路密度等8个评价因子。评价因子的信息量通过信息量法计算得出,将关联度值作为各因子叠加的权重值,进一步构建灰色关联分析-信息量模型,开展永胜县泥石流易发性评价研究。结果表明:泥石流灾害极高易发区频率比高达4.06,接受者操作特征曲线(receiver operating characteristic curve, ROC)下的面积为0.818,说明选取的8个评价因子以及灰色关联分析-信息量评价模型对永胜县泥石流灾害易发性评价研究具有较好的预测能力,得到的评价结果也对当地的泥石流灾害防治工作提供了一定的参考作用。

泥石流  /  灰色关联分析  /  信息量法  /  子流域单元  /  易发性评价  /  永胜县

In order to study the disaster susceptibility of debris flow in Yongsheng County, the research area was Yongsheng County of Lijiang City, Yunnan Province, and it was divided into 475 sub-watershed units. Grey correlation analysis method was used to calculate the correlation degree of each factor, and the factor with the lowest correlation degree was eliminated. The independence of factors was tested by collinearity diagnosis. In the end, eight factors including average slope, average annual maximum rainfall, average vegetation coverage, average elevation, average melton ratio, average water system density, average landslide core density and average road density were retained. The information volume of the factors was calculated by the information volume method, and the correlation degree value was taken as the weight value of the superposition of each factor. The grey correlation analysis-information volume model was further constructed to carry out the evaluation research on the vulnerability of debris flow in Yongsheng County. The results show this as follows. The requency ratio of debris flow disaster points in the extremely vulnerable area is as high as 4.06, and the area under the ROC (receiver operating characteristic) curve is 0.818, indicating that the selected eight factors and the grey correlation analysation-information volume method have good forecasting ability for the evaluation of debris flow disaster vulnerability. The results can also be used as reference for the prevention and control of debris flow disaster in Yongsheng County.

debris-flow  /  gray relational analysis  /  information volume method  /  unit factors of catchments  /  susceptibility assessment  /  Yongsheng County
常康泰, 赵志芳, 牟乔木, 杨永林, 胡云飞, 秦阳. 基于灰色关联分析-信息量法的永胜县泥石流易发性评价. 科学技术与工程, 2025 , 25 (3) : 933 -941 . DOI: 10.12404/j.issn.1671-1815.2402860
Kang-tai CHANG, Zhi-fang ZHAO, Qiao-mu MOU, Yong-lin YANG, Yun-fei HU, Yang QIN. Debris-flow Susceptibility Assessment in Yongsheng County Based on Gray Relational Analysis-information Volume Method[J]. Science Technology and Engineering, 2025 , 25 (3) : 933 -941 . DOI: 10.12404/j.issn.1671-1815.2402860
正文
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中国常见的地质灾害中,泥石流是一种频率极高的灾害[1]。它的特点包括突发性、高速流动、流量大以及强大的破坏力等,对人们的生命安全和财产安全带来了巨大的威胁。因此,进行泥石流易发性评价可以识别出研究区内的泥石流高易发区域,可以清楚地揭示泥石流灾害的地理空间分布情况,为灾害预警工作和灾害治理工作提供较为准确的依据。
泥石流易发性与植被覆盖、降水情况、地形地貌、地层岩性、人为因素等相关性较高,评价模型有信息量法、层次分析法、确定性系数、证据权重、逻辑回归以及机器学习等。评价质量的好坏与评价因子的选取、评价模型的构建有着极为密切的关系,众多学者根据不同的评价因子[2-3]以及评价模型[4-6]对泥石流灾害易发性开展了大量研究。周粤等[7]从地质、地貌、降雨量、物源条件、人类工程活动等方面选取了8种评价因子,并采用层次分析法确定众多因子的权重值。但层次分析法在计算评价因子的权重时需要构造判断矩阵,在分析过程中极易受到个人主观判断的影响。吴文建等[8]选定流域面积、主沟长度、流域相对高差、补给长度比、植被覆盖率、24 h最大降雨量等6个因子,采用灰色关联分析法开展泸水县银坡河泥石流的危险性评价,避免了人为因素的干扰。张云波等[9]使用信息量法,对石阡县地质灾害进行易发性评价。信息量法虽然具有操作简单,稳定性较好的优点,但确不能计算出各个评价因子的权重,无法指出因子对评价结果的贡献度。机器学习方法具有着较为优秀的非线性预测能力,目前已经成为地质灾害评价的主流方法之一。例如,李坤等[10]使用误差反向传播(back propagation, BP)神经网络,采用水文分析法和曲率分水岭法这两种斜坡单元划分方法,开展昆明市东川区泥石流易发性研究。机器学习精度相比传统方法精度较高,但存在样本训练、参数调试等烦琐的问题[11]。灰色关联分析-信息量法继承了信息量法的优点,并且采用灰色关联分析计算得到的关联度对评价因子进行赋权,解决了信息量模型无法计算因子权重的问题,避免了层次分析法中专家打分部分主观性过强的缺陷。
泥石流易发性评价可以基于栅格单元或者流域单元,以子流域作为评价单元,能更好地反映泥石流的真实地貌单元特征,更贴合实际情况,因此这种方法在近几年使用更为广泛。如孙滨等[12]在云南东川地区采用子流域单元进行泥石流的易发性评价工作。张书豪等[13]将研究区划分为1 570个子流域单元,在云南迪庆金沙江河谷一带进行泥石流的易发性研究。而使用栅格单元进行评价,如坡向、坡度和高程等因子进行叠加分析,这种评价方法割离了灾害与环境之间的关系,并且往往无法与实地环境保持一致。相比之下,根据研究区的地形地貌,将其划分为众多子流域单元,这种方法可以反映自然环境的真实情况,因此更符合泥石流发育的环境特征。
综上所述,以云南省丽江市永胜县为例,结合地理信息系统(geographic information system, GIS)环境,采用泥石流子流域作为评价单元,选择灰色关联分析法和信息量法对永胜县进行泥石流易发性评价,并采用频率比模型评判结果的合理性与准确性,为当地的泥石流灾害防治工作提供了一定的参考。
1 研究区概况及数据来源
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1.1 研究区概况
永胜县县境位于云南省丽江市东南部,地处长江上游,在东经100°22'~101°11',北纬25°59'~27°04',全县幅员面积约为4 952 km2,位置如图1所示。永胜县地跨横断山脉和滇西北高原两个地貌单元,存在多种地貌类型,地质环境复杂。
金沙江由西北部入境,从东部流出,境内河网密布,各级河流构成羽状水系。地势东北高,西南低, 最高海拔3 963.5 m,最低海拔1 056 m,相对高差达2 907.5 m。全境地处低纬高海拔地区,气候以北亚热带山地季风气候为主,年平均降水量约为843.6 mm,为泥石流提供了水源条件。除此之外,人类活动和地质环境也为泥石流的形成提供了条件,造成研究区内泥石流活动频发。
1.2 数据来源
本文中使用的主要数据如下:①30 m分辨率的数字高程模型(digital elevation model, DEM);②永胜县地质灾害统计清单;③包含研究区共3景Landsat8影像;④道路、断裂、水系、降雨数据。数据来源如表1所示。
2 研究方法
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2.1 子流域单元划分
本文中使用ArcGIS软件中的水文分析工具,经过填洼处理、流向计算、流量计算、定义河网、生成流域等步骤,并对不合理的流域边界进行修改,最终得到475个子流域单元。其中平均流域面积为10.37 km2,最大流域面积为82.50 km2,最小流域面积为1.45 km2,研究区泥石流子流域分布如图1所示。
2.2 信息量法
信息量模型是一种进行地质灾害易发性评价的常用方法[14-15],计算各个评价因子每个级别对泥石流灾害发生的信息量,再进一步计算得到总信息量,信息量值越大,表示发生地质灾害的概率越大。总信息量计算公式为
$I=\sum_{i=1}^{n} \ln \frac{N_{i} / N}{S_{i} / S}$
式(1)中:I为总信息量;Ni为单个评价因子特定类别内的泥石流个数;N为研究区泥石流的总个数;Si为单个评价因子特定类别内的栅格面积;S为研究区总栅格面积。
2.3 灰色关联分析
灰色关联分析[16]是指对一个系统发展变化态势的定量描述和比较的方法,用于待评分指标与“参考指标”的相似程度,进而对评价指标进行评价。它反映了不同特征数据序列与参考数据序列之间的联系紧密程度,同样适用于非线性、较复杂的数据。
依据永胜县的地形地貌等环境特征,结合永胜县地质灾害调查成果,参考众多专家学者在泥石流评价中使用较为普遍的评价指标,选取流域平均坡度、流域多年平均最大降雨量、流域平均植被覆盖度、流域平均高程、流域平均melton比率、流域平均水系密度、流域平均崩滑核密度、流域平均道路密度、流域平均断裂密度等9项指标作为泥石流易发性评价因子。采用SPSSPRO数据分析软件的综合评价方法,计算评价因子与待测目标的灰色关联度,将关联度值从高到低进行排名,选取关联度较大的因子开展永胜县泥石流易发性评价工作。具体步骤如下。
(1)定义A(m,n)为灰色关联系数的数据矩阵,其中m(m=1,2,…,i)为参考数据序列,n(n=1,2,…,j)为特征数据序列。对数据进行无量纲化,计算公式为
B(m,n)= A ( m , n ) 1 i ∑ m = 1 i A ( m , n )
(2)计算关联系数,定义参考数据为B(a,n),比较数据为B(m,n),则关联系数计算公式为
ε(m,n)=[minmminn B ( a , n ) - B ( m , n )+ρmaxmmaxn B ( a , n ) - B ( m , n )]/[ B ( a , n ) - B ( m , n )+ρmaxmmaxn B ( a , n ) - B ( m , n )]
式(3)中:ρ为分辨系数,ρ越小,分辨力越大,一般ρ的取值区间为(0,1),通常取ρ=0.5。
(3)计算关联度。计算公式为
R(m)= 1 i ∑ m = 1 i ε(m,n)
由表2可得,流域平均断裂密度关联度结果较低,因此,本文中剔除流域平均断裂密度这一评价因子,保留剩下的8项评价因子进行评价工作。
3 评价指标体系
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3.1 评价指标
根据灰色关联分析的结果,选定流域平均坡度、流域多年平均最大降雨量、流域平均植被覆盖度、流域平均高程、流域平均melton比率、流域平均水系密度、流域平均崩滑核密度、流域平均道路密度共8种连续型评价因子作为永胜县泥石流易发性评价指标,采用自然间断点法进行分级,并在结合研究区内灾害密度的情况下,对分级数值进行调整。
(1)流域平均坡度:坡度主要反映了研究区内各个位置的陡峭程度。坡度值越大,研究区的地势就越陡,泥石流灾害发生的频率就越大,反之则越小。以DEM计算得到的研究区坡度,计算各个子流域单元的平均坡度,分为5个等级,如图2(a)所示。研究区坡度在40°以内,大部分区域在15°~ 30°,说明该地区大部分地区为斜坡及陡坡,但也存在一定的平坦区域。如图3(a)所示,极大多数泥石流灾害发生在15°~ 30°这一区域内,数量达158个,约占总数的88%。
(2)流域多年平均最大降雨量:降雨可以为泥石流的发育提供水源条件,是泥石流发育的重要影响因素之一。降雨量越大,泥石流发生的概率也越大。将研究区内15个降雨观测站多年最大降雨量取平均值,采用克里金插值法对降雨数据进行插值处理,并计算各个子流域单元的平均降雨量,将其分为5个等级,如图2(b)所示。随着最大降雨量的增大,泥石流的点密度也随之增大,如图3(b)所示。
(3)流域平均植被覆盖度:植被覆盖度是指某一地区被植被所覆盖的程度,是衡量地表植被状况的重要指标之一,通常用百分比表示。值越高表明研究区的植被覆盖程度越高,反之则值越低。通过处理覆盖研究区的Landsat8影像数据,计算目标区域的归一化植被指数(normalized difference vegetation index, NDVI),并通过波段运算得到植被覆盖度。根据研究区的植被覆盖度,计算各个子流域单元的平均植被覆盖度,分为5个等级,如图2(c)所示。从总体趋势来看,泥石流灾害数量和密度的值随着植被覆盖度值的增大而增大,如图3(c)所示。这种情况不符合一般的认知,究其原因主要是因为植被覆盖度高的地方多为高山峡谷地貌区,较容易发生泥石流灾害。植被覆盖度低的区域主要是金沙江流域范围以及城镇等人口密集地区,在水系流域范围内较易发生灾害,而在城镇地区不易发生,因此出现了先升后降再升的变化趋势。
(4)流域平均高程:研究区位于云贵高原,是第一阶梯向第二阶梯的过渡地区,植被垂直分带以及岩层风化差异明显,对泥石流灾害的发生有着较大的影响,因此选取平均高程作为评价因子之一。以DEM计算得到的研究区高程,计算各个子流域单元的平均高程,分为5个等级,如图2(d)所示。经统计可知,灾害点密集分布区间在1 700~2 000 m高程范围内,如图3(d)所示。
(5)流域平均melton比率:melton比率在一定程度上能够反映出泥沙从形成区开始沿着流通区运输到堆积区整个过程地势的陡峭变化,它的值越大表明流域的地势越陡峭,引发泥石流的可能性越大;值越小表明流域的地势越平缓,引发泥石流的可能性越小。计算公式为
RM= d H S
式(5)中:S为子流域面积;H为子流域高差。
本文中计算各个子流域单元的平均melton比率,并分为5个等级。如图3(e)所示,随着melton比率的增大,泥石流灾害密度总体呈增大趋势。
(6)流域平均水系密度:河流同降雨量一样,也为泥石流的发育提供一定的水源条件,并且河流两岸的岩土体常年受到水流的冲刷和侵蚀,更易引发泥石流灾害。本文中通过计算研究区内水系的线密度,进一步得到各个子流域单元的平均水系密度,将流域平均水系密度分为5个等级,如图2(f)所示。由统计可得,泥石流灾害主要分布在0.66~0.81 km/km2范围内,数量为55个,点密度为0.051 个/km2。
(7)流域平均崩滑核密度:在一定的水源条件下,滑坡和崩塌经常会直接转化为泥石流或者形成新的泥石流,因此研究区崩塌、滑坡的数量以及分布对泥石流的生成发育有着较大的影响。核密度分析在本文中用于计算崩塌、滑坡在研究区中的密度,如果在某一区域内泥石流发生的次数较多则认为此区域内泥石流发生的频率高,反之则低。根据研究区崩滑核密度,计算各个子流域单元的平均崩滑核密度,本文中将其分为5个等级,如图2(g)所示。泥石流点密度随崩滑密度逐步增大,最大达到0.071 个/km2,如图3(g)所示。
(8)流域平均道路密度:泥石流发生的概率大小与修路工程活动有着较为紧密的关系,人类活动越频繁对地质环境的影响就越大,发生泥石流的概率也就越大。道路的分级处理方式与水系相同,本文通过计算研究区内道路的线密度,进一步得到各个子流域单元的平均道路密度,将流域平均道路密度分为5个等级,如图2(h)所示。泥石流灾害主要分布在密度>0.9 个/km2范围内,点密度最大达到0.055 个/km2,如图3(h)所示。
3.2 多重共线性诊断
多重共线性是指在多元线性回归模型中,各个变量之间存在高度线性相关关系,导致系数估计的准确性大打折扣,因此在构建泥石流的易发性评价指标体系时,要求各个评价因子之间不存在高度线性相关关系[17]。本文中使用SPSS27软件对评价因子进行了共线性诊断,诊断结果如表3所示。各评价因子的容差均大于0.4,方差膨胀因子(variance inflation factor, VIF)均小于3,说明因子之间基本不存在高度线性相关关系。
4 泥石流易发性评价
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4.1 评价结果
各评价因子的信息量值如表4所示。将各个评价因子的关联度视为各自的权重值,采用ArcGIS软件进行赋权叠加计算,再依据子流域单元进行分区统计,并且按照自然间断点法将统计后的结果分为4个等级,得到泥石流灾害易发性结果图,如图4所示。其中极高易发区信息量为0.65~2.83,高易发区信息量为-0.37~0.65,中易发区信息量为-1.85~-0.37,低易发区信息量为-4.59~-1.85。
从表5可知,研究区泥石流灾害极高易发区面积为779.02 km2,面积占比为15.78%,泥石流共发育65个,频率比为0.59;高易发区面积为1 778.19 km2,面积占比为36.01%,泥石流共发育75个,频率比为0.65;中易发区面积为1 957.30 km2,面积占比为39.64%,泥石流共发育37个,频率比为1.17;低易发区面积为522.91 km2,面积占比为8.57%,泥石流共发育3个,频率比为4.06。随着易发性等级的提高,各个易发性分区的频率比值逐渐增大,且极高易发区频率比远大于低易发区频率比,说明泥石流易发性评价结果较为合理。
从图4可以看出,泥石流灾害点的空间分布情况与高易发及以上的范围较一致,极高易发区主要分布在松坪乡、光华乡、大安乡、羊坪乡周围,除此之外,片角镇北部、东山乡南部、期纳镇东西两侧也是极高易发区所在的区域。这些地方地形陡峭,降雨充沛,大部分为高山峡谷区域,并且人类活动较为频繁,工程建设对研究区内自然环境影响相对较大,因此泥石流灾害发生的较为频繁。
4.2 精度评价
接受者操作特征曲线(receiver operating characteristic curve, ROC)可以直观地反映出泥石流累积预测比例和非泥石流累积预测比例的关系,全面、客观地评价出预测模型的准确性,因此较为广泛应用于地质灾害的评价之中[18]。为了验证永胜县泥石流地质灾害易发性评价分区结果的合理性,本文中采用ROC曲线来验证评价模型、易发性分区结果的合理性和科学性。
图5所示为本文评价模型的ROC曲线图, ROC曲线下与坐标轴围成的面积(area under curve, AUC)值为模型曲线以下直到横轴的面积部分,其越接近1.0,检测方法真实性越高。若AUC>0.5,则可以说明此曲线是有效的;若AUC>0.7,则可以说明预测模型的效果较好[19]。永胜县泥石流易发性评价的AUC=0.818,表示评价精度为81.8%,说明本文基于灰色关联分析-信息量法的泥石流易发性评价结果具有较高的准确度。
5 结论
收起
(1)本文构建了基于灰色关联分析-信息量法的泥石流易发性评价模型,并采用子流域单元作为评价单元。此评价模型可以防止过多的主观因素对评价结果造成干扰,弥补了信息量法不能定权的缺陷,评价结果较为准确,为永胜县泥石流灾害的防治工作提供参考。
(2)通过灰色关联分析,剔除掉关联度最低的流域平均断裂密度因子,最终确定了8个评价因子为:流域平均坡度、流域多年平均最大降雨量、流域平均植被覆盖度、流域平均高程、流域平均melton比率、流域平均水系密度、流域平均崩滑核密度、流域平均道路密度。
(3)对评价因子之间的相关性做了共线性诊断测试,容差均大于0.4,方差膨胀因子均小于3,说明各评价因子之间不存在高度线性相关关系。
(4)永胜县泥石流灾害大多数发生在极高、高易发区中,其中极高易发区面积为779.02 km2,面积占比为15.78%,泥石流共发育65个,个数占比为36.11%,频率比为4.06。而中易发区和低易发区面积共为2 480.21 km2,面积总占比为48.21%,泥石流共发育40个,个数总占比为22.23%。
(5)通过ROC曲线对泥石流灾害易发性评价结果进行检验,AUC值为0.818,准确度较高,表明本文选取的评价因子和使用的评价模型是比较合理的。
基金
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  • 云南省教育厅科学研究基金(2023Y0196)
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2025年第25卷第3期
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doi: 10.12404/j.issn.1671-1815.2402860
  • 接收时间：2024-04-19
  • 首发时间：2025-07-29
  • 出版时间：2025-01-28
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  • 收稿日期：2024-04-19
  • 修回日期：2024-07-18
基金
云南省教育厅科学研究基金(2023Y0196)
作者信息
    1.云南大学国际河流与生态安全研究院, 昆明 650050
    2.云南大学地球科学学院, 昆明 650050
    3.自然资源部三江成矿作用及资源勘查利用重点实验室, 昆明 650051
    4.云南省三江成矿与资源勘查利用重点实验室, 昆明 650051
    5.云南省国产高分卫星遥感地质工程研究中心, 昆明 650050
    6.云南省中老孟缅自然资源遥感监测国际联合实验室, 昆明 650051

通讯作者:

* 赵志芳(1971—),女,白族,云南香格里拉人,博士,教授,博士研究生导师。研究方向:资源与环境遥感监测、矿化蚀变遥感异常增强提取、边境地区国土资源遥感监测等。E-mail:。
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
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