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Article(id=1217789885193310922, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1217789884081820362, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2408609, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1731340800000, receivedDateStr=2024-11-12, revisedDate=1744905600000, revisedDateStr=2025-04-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1768273334072, onlineDateStr=2026-01-13, pubDate=1753632000000, pubDateStr=2025-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768273334072, onlineIssueDateStr=2026-01-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768273334072, creator=13701087609, updateTime=1768273334072, updator=13701087609, issue=Issue{id=1217789884081820362, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='21', pageStart='8761', pageEnd='9209', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1768273333807, creator=13701087609, updateTime=1768273602927, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1217791012932604619, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1217789884081820362, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1217791012932604620, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1217789884081820362, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=8805, endPage=8813, ext={EN=ArticleExt(id=1217789885566603981, articleId=1217789885193310922, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Meteorological Risk Early Warning of Geological Disasters Based on Information Value Method: An Example from Huangshui Basin of Qinghai Province, columnId=1156262729351549255, journalTitle=Science Technology and Engineering, columnName=Papers·Astronomy and Geosciences, runingTitle=null, highlight=null, articleAbstract=

In order to study the relationship between rainfall and regional geological disaster risk, construct the meteorological warning model of geological disasters in Huangshui Basin, and provide reference for the geological disaster warning work in the basin. Huangshui Basin in Qinghai Province was taken as an example, where the susceptibility to geological hazards was evaluated using the information value method. The independence of each factor was tested using the Pearson correlation coefficient, and the accuracy of the evaluation results was assessed through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Subsequently, a geological-rainfall coupling model was also established using the logistic regression method, incorporating rainfall data, to carry out geological disaster meteorological risk warning and verification. The results show that Huangshui Basin geological disasters of very high susceptibility area and high susceptibility area is mainly distributed in the river valley plain zone and its tributaries at all levels in the middle and lower reaches of the two sides, the region is a geological disaster area. The results of the susceptibility evaluation are assessed for precision, and an AUC value of 0.785 is found. Geological-rainfall coupled warning model in the susceptibility value, the day rainfall, the first 1 day rainfall, the first 2 days day rainfall, the first 2 days of rainfall and the first 3 days of rainfall on the disaster are all positive, the model's prediction accuracy for the occurrence of geological disasters is 84.2%, and the AUC value is 0.864. Based on the five typical geological disasters that occurred in Huangshui Basin on 29th August 2020, the established geological-rainfall coupling model was verified, and it is found that with the geological-rainfall coupling model warning, four geological disasters reach the first warning level, and the other one reaches the second warning level. Combined with the warning rainfall threshold, the model was used to achieve the early warning of the landslide in Hongyagou Village on 5 September 2024. It can be seen that the constructed meteorological early warning model for geological disasters in the Huangshui Basin has a good early warning effect and a relatively high prediction accuracy rate, which can provide references for disaster prevention and mitigation for relevant departments.

, correspAuthors=Jian-yun 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=Cheng-long YANG, Jian-yun ZHAO, Chang-jun XU, Feng CHEN, Yue-di HOU), CN=ArticleExt(id=1217789887164633838, articleId=1217789885193310922, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于信息量法的地质灾害气象风险预警:以青海省湟水流域为例, 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=

研究降雨与区域地质灾害风险的关系,构建湟水流域地质灾害气象预警模型,为该流域地质灾害预警工作提供参考。以青海省湟水流域为例,利用信息量法进行地质灾害易发性评价,采用皮尔逊相关系数对各因子进行独立性检验并利用受试者特征曲线(receiver operating characteristic curve,ROC)与曲线下的面积(area under curve,AUC)对评价结果进行精度评定;结合降雨资料,利用逻辑回归方法建立地质-降雨耦合模型开展地质灾害气象风险预警,并进行验证。结果表明: 湟水流域地质灾害的极高易发区与高易发区主要分布在河谷平原地带及其各级支流中下游两侧,该区域是地质灾害多发区;对易发性评价结果进行精度评定,AUC为0.785;地质-降雨耦合预警模型中的易发性值、当日降雨量、前1 d降雨量、前2 d降雨量及前3 d降雨量对灾害的影响均为正值,模型对地质灾害发生的预测准确率为84.2%,AUC为0.864;基于2020年8月29日湟水流域发生的5起典型地质灾害,对建立的地质-降雨耦合模型进行验证,发现以地质-降雨耦合模型预警,4起地质灾害达到一级预警等级,另外1起达到二级预警等级;结合预警雨量阈值,利用该模型实现对2024年9月5日红崖沟村山体滑坡的预警。可见所构建的湟水流域地质灾害气象预警模型预警效果良好,预测准确率较高,能为相关部门提供防灾减灾参考。

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* 赵健赟(1981—),男,汉族,甘肃张掖人,博士,副教授,硕士研究生导师。研究方向:对地观测技术与地质资源环境评价。E-mail:。
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杨成龙(2000—),男,汉族,江苏盐城人,硕士研究生。研究方向:地质灾害气象风险预警。E-mail:。

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杨成龙(2000—),男,汉族,江苏盐城人,硕士研究生。研究方向:地质灾害气象风险预警。E-mail:。

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杨成龙(2000—),男,汉族,江苏盐城人,硕士研究生。研究方向:地质灾害气象风险预警。E-mail:。

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Science Technology and Engineering, 2020, 20(21): 8460-8468., articleTitle=Landslide susceptibility evaluation based on coupled informative and logistic regression models, refAbstract=null)], funds=[Fund(id=1217860117425476090, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, awardId=2023-SF-122, language=CN, fundingSource=青海省重点研发与转化计划(2023-SF-122), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1217860107015213838, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, xref=1, ext=[AuthorCompanyExt(id=1217860107023602448, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, companyId=1217860107015213838, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 College of Geological Engineering, Qinghai University, Xining 810016, China), AuthorCompanyExt(id=1217860107061351187, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, companyId=1217860107015213838, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 青海大学地质工程学院, 西宁 810016)]), AuthorCompany(id=1217860107191374618, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, xref=2, ext=[AuthorCompanyExt(id=1217860107195568923, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, companyId=1217860107191374618, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 Qinghai Provincial Key Laboratory of Geospatial Information Technology and Application, Xining 810001, China), AuthorCompanyExt(id=1217860107203957532, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, companyId=1217860107191374618, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 青海省地理空间信息技术与应用重点实验室, 西宁 810001)]), AuthorCompany(id=1217860107317203749, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, xref=3, ext=[AuthorCompanyExt(id=1217860107321398054, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, companyId=1217860107317203749, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 College of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China), AuthorCompanyExt(id=1217860107329786663, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, companyId=1217860107317203749, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 武汉大学资源与环境科学学院, 武汉 430079)])], figs=[ArticleFig(id=1217860113109536926, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Fig.1, caption=Geographical location of the study area, figureFileSmall=SgFf6IybNRu43fWFklyPlw==, figureFileBig=iXM4qxSOE5N2QUzRrYfwaw==, tableContent=null), ArticleFig(id=1217860113231171753, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=图1, caption=研究区地理位置, figureFileSmall=SgFf6IybNRu43fWFklyPlw==, figureFileBig=iXM4qxSOE5N2QUzRrYfwaw==, tableContent=null), ArticleFig(id=1217860113377972414, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Fig.2, caption=Vulnerability zones, figureFileSmall=NZa+QL9JcggrEDRn0e3Tcw==, figureFileBig=jvQrqnwSXVAQRmisyHns7g==, tableContent=null), ArticleFig(id=1217860113482830032, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=图2, caption=易发性区划, figureFileSmall=NZa+QL9JcggrEDRn0e3Tcw==, figureFileBig=jvQrqnwSXVAQRmisyHns7g==, tableContent=null), ArticleFig(id=1217860113587687643, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Fig.3, caption=ROC curve and AUC value, figureFileSmall=GrZaivhb32UFu2Y2zgO+cw==, figureFileBig=iHgkG8Gi5CAy6dumMnOZRg==, tableContent=null), ArticleFig(id=1217860113688350950, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=图3, caption=ROC曲线与AUC值

X轴为假阳性率,即1-特异度;Y轴为真阳性率,即敏感性

, figureFileSmall=GrZaivhb32UFu2Y2zgO+cw==, figureFileBig=iHgkG8Gi5CAy6dumMnOZRg==, tableContent=null), ArticleFig(id=1217860113877094647, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Fig.4, caption=Rainfall for the day and cumulative rainfall for the previous 3 days, figureFileSmall=kNw84hhdjzn5s3JC+KFBKA==, figureFileBig=JOV3Ln4n0p67ZYgaYG+KVQ==, tableContent=null), ArticleFig(id=1217860113973563655, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=图4, caption=当日降雨量与前3 d累计降雨量, figureFileSmall=kNw84hhdjzn5s3JC+KFBKA==, figureFileBig=JOV3Ln4n0p67ZYgaYG+KVQ==, tableContent=null), ArticleFig(id=1217860114111975703, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Fig.5, caption=ROC curve and AUC value, figureFileSmall=tytXG9lwZSBnbQTWTChF1A==, figureFileBig=0UFBfJrpmbxdoVCtMGfLcw==, tableContent=null), ArticleFig(id=1217860114342662443, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=图5, caption=ROC曲线与AUC值, figureFileSmall=tytXG9lwZSBnbQTWTChF1A==, figureFileBig=0UFBfJrpmbxdoVCtMGfLcw==, tableContent=null), ArticleFig(id=1217860114472685883, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Fig.6, caption=Coupled geology-rainfall model warning (2020-08-29), figureFileSmall=Qm1pBiDizk19BbchJPblZQ==, figureFileBig=h9CowgPy6lB2sVKEYiw0ig==, tableContent=null), ArticleFig(id=1217860114573349193, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=图6, caption=地质-降雨耦合模型预警(2020-08-29), figureFileSmall=Qm1pBiDizk19BbchJPblZQ==, figureFileBig=h9CowgPy6lB2sVKEYiw0ig==, tableContent=null), ArticleFig(id=1217860114678206808, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Fig.7, caption=Early warning results for 5 th September 2024, figureFileSmall=Tk/+eb0rnqbLMWjGxZ9FRQ==, figureFileBig=d8VJW9129AcmzNet9g55Pg==, tableContent=null), ArticleFig(id=1217860115999412587, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=图7, caption=2024年9月5日预警结果, figureFileSmall=Tk/+eb0rnqbLMWjGxZ9FRQ==, figureFileBig=d8VJW9129AcmzNet9g55Pg==, tableContent=null), ArticleFig(id=1217860116125241715, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Table 1, caption=

Correlation between factors

, figureFileSmall=null, figureFileBig=null, tableContent=
因子 地貌类型 地层岩性 土地利用 距水系距离 距道路距离 NDVI 坡度 高程
地貌类型 1 0.075 -0.017 -0.220 -0.197 -0.240 0.008 -0.443
地层岩性 0.075 1 -0.007 -0.188 -0.006 -0.065 -0.024 -0.111
土地利用 -0.017 -0.007 1 0.000 0.137 -0.062 0.272 -0.153
距水系距离 -0.220 -0.188 0.000 1 0.223 0.224 0.074 0.368
距道路距离 -0.197 -0.006 0.137 0.223 1 0.092 0.134 0.204
NDVI -0.240 -0.065 -0.062 0.224 0.092 1 -0.022 0.341
坡度 0.008 -0.024 0.272 0.074 0.134 -0.022 1 0.023
高程 -0.443 -0.111 -0.153 0.368 0.204 0.341 0.023 1
), ArticleFig(id=1217860116238487932, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=表1, caption=

因子间相关性

, figureFileSmall=null, figureFileBig=null, tableContent=
因子 地貌类型 地层岩性 土地利用 距水系距离 距道路距离 NDVI 坡度 高程
地貌类型 1 0.075 -0.017 -0.220 -0.197 -0.240 0.008 -0.443
地层岩性 0.075 1 -0.007 -0.188 -0.006 -0.065 -0.024 -0.111
土地利用 -0.017 -0.007 1 0.000 0.137 -0.062 0.272 -0.153
距水系距离 -0.220 -0.188 0.000 1 0.223 0.224 0.074 0.368
距道路距离 -0.197 -0.006 0.137 0.223 1 0.092 0.134 0.204
NDVI -0.240 -0.065 -0.062 0.224 0.092 1 -0.022 0.341
坡度 0.008 -0.024 0.272 0.074 0.134 -0.022 1 0.023
高程 -0.443 -0.111 -0.153 0.368 0.204 0.341 0.023 1
), ArticleFig(id=1217860116464980371, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Table 2, caption=

Evaluation factor rating informative values

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 分类标准 计算的信息量值
Ni Si I
高程/m 1 600~2 450 358 2 711.470 1.938
2 450~2 850 646 4 355.890 2.176
2 850~3 250 54 3 613.870 0.219
3 250~3 650 2 3 016.010 0.010
>3 650 0 1 857.511 0
坡度/(°) <8 395 4 209.121 1.377
8~15 319 3 806.189 1.230
15~23 232 3 882.589 0.877
23~32 91 2 626.844 0.508
32~52 23 1 021.979 0.330
>52 0 8.030 0
土地利用 水域 1 28.642 0.512
林地 3 176.061 0.250
耕地 213 2 101.390 1.487
建筑区 273 1 072.350 3.736
裸地 2 187.947 0.156
牧场 568 11 978.500 0.696
冰雪 0 9.866 0
地貌类型 高海拔大起伏山地 95 5 004.070 -1.278
高海拔中起伏山地 0 182.948 0
中高海拔大起伏山地 0 16.128 0
中高海拔丘陵 0 85.188 0
中高海拔洪积平原 39 1 202.360 -0.742
中高海拔黄土梁峁 126 1 970.030 -0.063
中高海拔中起伏山地 467 4 426.000 0.437
中海拔冲积洪积平原 333 2 668.040 0.605
地层岩性 变质岩 65 3 223.670 -1.218
硅质碎屑沉积岩 173 2 333.090 0.084
碳酸盐沉积岩 13 559.703 -1.076
混合沉积岩 301 3 661.740 0.188
松散沉积岩 400 3 517.250 0.512
基性火山岩 3 150.642 -1.230
酸性深成岩 105 1 918.300 -0.219
基性深成岩 0 190.374 0
NDVI <0.3 91 856.710 1.559
0.3~0.5 360 2 919.870 1.809
0.5~0.7 438 5 821.205 1.104
>0.7 171 5 956.972 0.421
距道路
距离/m		 <50 155 766.891 2.966
50~150 211 1 157.320 2.675
150~250 145 788.594 2.698
250~500 187 1 656.820 1.656
500~1 000 113 2 509.390 0.661
>1 000 249 8 675.800 0.421
距水系
距离/m		 <300 125 687.752 2.667
300~600 89 629.984 2.073
600~900 85 614.973 2.028
900~1 200 60 595.647 1.478
>1 200 701 13 026.400 0.790
), ArticleFig(id=1217860116607586722, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=表2, caption=

评价因子分级信息量值

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 分类标准 计算的信息量值
Ni Si I
高程/m 1 600~2 450 358 2 711.470 1.938
2 450~2 850 646 4 355.890 2.176
2 850~3 250 54 3 613.870 0.219
3 250~3 650 2 3 016.010 0.010
>3 650 0 1 857.511 0
坡度/(°) <8 395 4 209.121 1.377
8~15 319 3 806.189 1.230
15~23 232 3 882.589 0.877
23~32 91 2 626.844 0.508
32~52 23 1 021.979 0.330
>52 0 8.030 0
土地利用 水域 1 28.642 0.512
林地 3 176.061 0.250
耕地 213 2 101.390 1.487
建筑区 273 1 072.350 3.736
裸地 2 187.947 0.156
牧场 568 11 978.500 0.696
冰雪 0 9.866 0
地貌类型 高海拔大起伏山地 95 5 004.070 -1.278
高海拔中起伏山地 0 182.948 0
中高海拔大起伏山地 0 16.128 0
中高海拔丘陵 0 85.188 0
中高海拔洪积平原 39 1 202.360 -0.742
中高海拔黄土梁峁 126 1 970.030 -0.063
中高海拔中起伏山地 467 4 426.000 0.437
中海拔冲积洪积平原 333 2 668.040 0.605
地层岩性 变质岩 65 3 223.670 -1.218
硅质碎屑沉积岩 173 2 333.090 0.084
碳酸盐沉积岩 13 559.703 -1.076
混合沉积岩 301 3 661.740 0.188
松散沉积岩 400 3 517.250 0.512
基性火山岩 3 150.642 -1.230
酸性深成岩 105 1 918.300 -0.219
基性深成岩 0 190.374 0
NDVI <0.3 91 856.710 1.559
0.3~0.5 360 2 919.870 1.809
0.5~0.7 438 5 821.205 1.104
>0.7 171 5 956.972 0.421
距道路
距离/m		 <50 155 766.891 2.966
50~150 211 1 157.320 2.675
150~250 145 788.594 2.698
250~500 187 1 656.820 1.656
500~1 000 113 2 509.390 0.661
>1 000 249 8 675.800 0.421
距水系
距离/m		 <300 125 687.752 2.667
300~600 89 629.984 2.073
600~900 85 614.973 2.028
900~1 200 60 595.647 1.478
>1 200 701 13 026.400 0.790
), ArticleFig(id=1217860116712444328, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Table 3, caption=

Variables in the coupled geological-rainfall model

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时间 B Wald Sig exp(B)
前3 d 0.093 5.776 0.000 1.098
前2 d 0.105 5.380 0.000 1.110
前1 d 0.090 5.417 0.000 1.094
当日 0.113 6.770 0.000 1.119
易发性值 0.574 56.855 0.000 1.775
常量 -5.105 90.916 0.000 0.006
), ArticleFig(id=1217860116850856377, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=表3, caption=

地质-降雨耦合模型中的变量

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时间 B Wald Sig exp(B)
前3 d 0.093 5.776 0.000 1.098
前2 d 0.105 5.380 0.000 1.110
前1 d 0.090 5.417 0.000 1.094
当日 0.113 6.770 0.000 1.119
易发性值 0.574 56.855 0.000 1.775
常量 -5.105 90.916 0.000 0.006
), ArticleFig(id=1217860117027017166, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=EN, label=Table 4, caption=

Typical geologic hazards in 2020

, figureFileSmall=null, figureFileBig=null, tableContent=
地点 灾害 发生时间 规模/m3
台子乡出路沟村6社 崩塌 6月6日 63
城中区南酉山路 滑坡 8月3日 4 500
拦隆口镇下红土沟村 崩塌 8月23日 2 520
拦隆口镇下红林村 崩塌 8月24日 120
大通县桥头镇窑庄村 崩塌 8月26日 2 618
拦隆口镇下红土沟村 滑坡 8月29日 450
拦隆口镇下红土沟村 滑坡 8月29日 320
桦林乡关巴村4社 崩塌 8月29日 30
黄家寨镇大哈门村3社 崩塌 8月29日 84
黄家寨镇上柴村2社 崩塌 8月29日 390
), ArticleFig(id=1217860117165429217, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789885193310922, language=CN, label=表4, caption=

2020年典型地质灾害情况

, figureFileSmall=null, figureFileBig=null, tableContent=
地点 灾害 发生时间 规模/m3
台子乡出路沟村6社 崩塌 6月6日 63
城中区南酉山路 滑坡 8月3日 4 500
拦隆口镇下红土沟村 崩塌 8月23日 2 520
拦隆口镇下红林村 崩塌 8月24日 120
大通县桥头镇窑庄村 崩塌 8月26日 2 618
拦隆口镇下红土沟村 滑坡 8月29日 450
拦隆口镇下红土沟村 滑坡 8月29日 320
桦林乡关巴村4社 崩塌 8月29日 30
黄家寨镇大哈门村3社 崩塌 8月29日 84
黄家寨镇上柴村2社 崩塌 8月29日 390
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基于信息量法的地质灾害气象风险预警:以青海省湟水流域为例
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杨成龙 1 , 赵健赟 1, * , 许长军 2 , 陈风 1 , 侯月迪 3
科学技术与工程 | 论文·天文学、地球科学 2025,25(21): 8805-8813
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科学技术与工程 | 论文·天文学、地球科学 2025, 25(21): 8805-8813
基于信息量法的地质灾害气象风险预警:以青海省湟水流域为例
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杨成龙1 , 赵健赟1, * , 许长军2, 陈风1, 侯月迪3
作者信息
  • 1 青海大学地质工程学院, 西宁 810016
  • 2 青海省地理空间信息技术与应用重点实验室, 西宁 810001
  • 3 武汉大学资源与环境科学学院, 武汉 430079

    • 杨成龙(2000—),男,汉族,江苏盐城人,硕士研究生。研究方向:地质灾害气象风险预警。E-mail:。

通讯作者:

* 赵健赟(1981—),男,汉族,甘肃张掖人,博士,副教授,硕士研究生导师。研究方向:对地观测技术与地质资源环境评价。E-mail:。
Meteorological Risk Early Warning of Geological Disasters Based on Information Value Method: An Example from Huangshui Basin of Qinghai Province
Cheng-long YANG1 , Jian-yun ZHAO1, * , Chang-jun XU2, Feng CHEN1, Yue-di HOU3
Affiliations
  • 1 College of Geological Engineering, Qinghai University, Xining 810016, China
  • 2 Qinghai Provincial Key Laboratory of Geospatial Information Technology and Application, Xining 810001, China
  • 3 College of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
出版时间: 2025-07-28 doi: 10.12404/j.issn.1671-1815.2408609
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摘要
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研究降雨与区域地质灾害风险的关系,构建湟水流域地质灾害气象预警模型,为该流域地质灾害预警工作提供参考。以青海省湟水流域为例,利用信息量法进行地质灾害易发性评价,采用皮尔逊相关系数对各因子进行独立性检验并利用受试者特征曲线(receiver operating characteristic curve,ROC)与曲线下的面积(area under curve,AUC)对评价结果进行精度评定;结合降雨资料,利用逻辑回归方法建立地质-降雨耦合模型开展地质灾害气象风险预警,并进行验证。结果表明: 湟水流域地质灾害的极高易发区与高易发区主要分布在河谷平原地带及其各级支流中下游两侧,该区域是地质灾害多发区;对易发性评价结果进行精度评定,AUC为0.785;地质-降雨耦合预警模型中的易发性值、当日降雨量、前1 d降雨量、前2 d降雨量及前3 d降雨量对灾害的影响均为正值,模型对地质灾害发生的预测准确率为84.2%,AUC为0.864;基于2020年8月29日湟水流域发生的5起典型地质灾害,对建立的地质-降雨耦合模型进行验证,发现以地质-降雨耦合模型预警,4起地质灾害达到一级预警等级,另外1起达到二级预警等级;结合预警雨量阈值,利用该模型实现对2024年9月5日红崖沟村山体滑坡的预警。可见所构建的湟水流域地质灾害气象预警模型预警效果良好,预测准确率较高,能为相关部门提供防灾减灾参考。

地质灾害  /  风险预警  /  气象  /  信息量法  /  逻辑回归  /  湟水流域

In order to study the relationship between rainfall and regional geological disaster risk, construct the meteorological warning model of geological disasters in Huangshui Basin, and provide reference for the geological disaster warning work in the basin. Huangshui Basin in Qinghai Province was taken as an example, where the susceptibility to geological hazards was evaluated using the information value method. The independence of each factor was tested using the Pearson correlation coefficient, and the accuracy of the evaluation results was assessed through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Subsequently, a geological-rainfall coupling model was also established using the logistic regression method, incorporating rainfall data, to carry out geological disaster meteorological risk warning and verification. The results show that Huangshui Basin geological disasters of very high susceptibility area and high susceptibility area is mainly distributed in the river valley plain zone and its tributaries at all levels in the middle and lower reaches of the two sides, the region is a geological disaster area. The results of the susceptibility evaluation are assessed for precision, and an AUC value of 0.785 is found. Geological-rainfall coupled warning model in the susceptibility value, the day rainfall, the first 1 day rainfall, the first 2 days day rainfall, the first 2 days of rainfall and the first 3 days of rainfall on the disaster are all positive, the model's prediction accuracy for the occurrence of geological disasters is 84.2%, and the AUC value is 0.864. Based on the five typical geological disasters that occurred in Huangshui Basin on 29th August 2020, the established geological-rainfall coupling model was verified, and it is found that with the geological-rainfall coupling model warning, four geological disasters reach the first warning level, and the other one reaches the second warning level. Combined with the warning rainfall threshold, the model was used to achieve the early warning of the landslide in Hongyagou Village on 5 September 2024. It can be seen that the constructed meteorological early warning model for geological disasters in the Huangshui Basin has a good early warning effect and a relatively high prediction accuracy rate, which can provide references for disaster prevention and mitigation for relevant departments.

geological hazards  /  risk warning  /  meteorology  /  information value method  /  logistic regression  /  Huangshui Basin
杨成龙, 赵健赟, 许长军, 陈风, 侯月迪. 基于信息量法的地质灾害气象风险预警:以青海省湟水流域为例. 科学技术与工程, 2025 , 25 (21) : 8805 -8813 . DOI: 10.12404/j.issn.1671-1815.2408609
Cheng-long YANG, Jian-yun ZHAO, Chang-jun XU, Feng CHEN, Yue-di HOU. Meteorological Risk Early Warning of Geological Disasters Based on Information Value Method: An Example from Huangshui Basin of Qinghai Province[J]. Science Technology and Engineering, 2025 , 25 (21) : 8805 -8813 . DOI: 10.12404/j.issn.1671-1815.2408609
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近年来,由于极端的气象条件以及大范围和不规律的人类活动等因素,地质灾害在全球频发[1]。青海省湟水流域由于春秋季降水偏少,夏季偏多,降水分布不均,总体呈现旱涝两极分化、旱涝交替并存特征,高温、强降水等各类极端事件频发。2022年湟水流域发生地质灾害109起,占全省总数的75.7%,是青海省地质灾害重点防治区域之一。
地质灾害预警的研究起源于20世纪末,美国旧金山率先建立滑坡实时预报体系[2],中国香港建立了世界上第一个滑坡灾害预警系统[3]。经过多年的发展,国内形成了3种可供选择的预警模型:临界降水阈值模型[4-5]、基于地质灾害危险性的阈值模型[6-8]与动力预警模型[9]。其中临界降水阈值模型以降雨为单一触发机制,不能表达地质环境对地质灾害的影响,适用于地质环境比较单一的小区域。动力预警模型是基于物理机制的预警模型,该方法尚处于研究阶段[10],适用于面积较小或特别重要的局部区域。实际预警模型宜采用基于地质灾害危险性的阈值模型[11],它充分考虑了地质环境因素与降雨的相互作用关系[12],适用于面积较大或地质环境条件比较复杂的区域[13]。
青海省地质灾害气象风险预警的研究虽然取得了一定的成果,但模型多采用临界降水阈值模型,缺乏综合考虑湟水流域地质和气象因素的地质灾害气象风险预警模型。现基于信息量法进行地质灾害易发性评价,结合降雨资料利用逻辑回归方法建立地质-降雨耦合预警模型,并根据研究区地质灾害历史发生案例进行检验以提高湟水流域地质灾害气象预警能力。
1 研究区概况
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湟水流域地处黄土高原与青藏高原的过渡带(100° 40' ~103° 03'E,36°02' ~37°25'N),是黄河流域西北隅的重要支流流域[14],面积约1.6×104 km2,如图1所示。湟水流域地处青海省区域发展战略中的核心区域,是全省政治、经济、文化、交通中心和主要的工农业生产基地。
湟水流域地形地貌特征复杂多变,包括侵蚀堆积河谷、侵蚀剥蚀低山丘陵和构造侵蚀中高山等多种类型[15];湟水流域土地覆被类型多样,空间异质性大,受人类活动及高寒条件的影响和制约,生态环境相当脆弱。降雨主要集中在夏季,尤以6—9月汛期最为显著。同时,受高海拔与地形影响,降雨强度往往较大。
2 数据与方法
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2.1 数据来源与评价单元划分
湟水流域历史灾害点数据来源于地质灾害详细调查资料及野外调查成果;数字高程模型(digital elevation model,DEM)来源于中国科学院资源环境科学与数据中心,分辨率为12.5 m。降雨及土地利用数据来源于国家青藏高原数据中心(http://data.tpdc.ac.cn),分辨率为1 000 m。地貌、地层岩性与NDVI数据来源于中国科学院资源环境科学与数据中心;水系及路网数据来源于OSM官网(https://www.openstreetmap.org/)。
利用上述8个因子作为易发性区划的评价指标并绘制地质灾害易发性区划图,选取30 m×30 m的栅格单元作为基本评价单元,各数据分辨率统一为30 m×30 m,坐标统一为GCS_WGS_1984。
2.2 研究方法
基于湟水流域的滑坡编录数据、气象水文资料、基础地质资料与野外勘察资料等多源数据,利用信息量法进行易发性评价,选用逻辑回归算法建立地质-降雨耦合模型,并利用受试者特征曲线(receiver operating characteristic curve,ROC)与曲线下的面积(area under curve,AUC)对结果进行精度评定,开展湟水流域地质灾害气象风险预警工作。
2.2.1 易发性评价方法
在地质灾害易发性评价领域,模型的选择至关重要,常用的地质灾害易发性评价模型有频率比模型[16]、随机森林[17]、支持向量机[18]、信息量模型[19-20]等,其中信息量模型因能较好地处理多因子组合带来的复杂性,对易发性评价因素的标志状态分类没有特殊要求并在单元划分数量较多的地质灾害易发性评价中具有优势而被广泛运用。信息量法主要研究各评价因子对地质灾害发生的贡献率,贡献率越高则信息量值越大,所在区域发生地质灾害的可能性就越大,反之亦然[21]。公式如下。
I=ln N i / N S i / S
式(1)中:Ni为研究区某评价因子特定类别所对应的地质灾害点数量;N为研究区内地质灾害点总数;Si为研究区某评价因子特定类别所占面积;S为研究区总面积;I为计算的信息量值。
2.2.2 预警方法
一直以来,确定有效降雨量时数是地质灾害气象预警的一个难点,逻辑回归模型[22-23]可以通过显著性检验自动去除相关性差的因子,从而解决了这一问题。另外,逻辑回归模型具有假设简单、限制条件少、操作简单等明显优势。因而最终选取逻辑回归模型作为地质灾害气象预警模型中地质环境因子与气象因子耦合的基本方法。计算公式如下。
P= e x p ( Z ) 1 + e x p ( Z )
Z=ln P 1 - P=A+BX+ε
式中:P为地质灾害发生概率;1-P为地质灾害不发生的概率;X为影响地质灾害发生的因子;B为对应的逻辑回归系数;A为常量;ε为误差项。
2.2.3 精度验证方法
通常可利用受试者特征曲线(ROC)评估模型的精度及预测性能,曲线下的面积(AUC)越大代表模型精度越高,AUC的取值区间为(0.5,1]。当AUC≤0.5时,模型无效;0.5<AUC≤0.7时,模型精度较一般;0.7<AUC≤0.9时,模型精度较高;AUC>0.9时,模型精度很高。
3 地质灾害易发性评价
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3.1 地质灾害易发性区划
选取高程、坡度、土地利用、地貌类型、地层岩性、归一化植被指数(normalized difference vegetation index,NDVI)、距道路距离及距水系距离作为评价因子,利用栅格数据提取工具提取各因子值,进而分析判断各因子间相互依赖的程度。分析发现:各因子间的皮尔逊相关系数的绝对值均低于0.5,表明它们之间具有相对较强的独立性,如表1所示。故保留上述8个因子进行分析。
根据自然断点法对各评价因子进行分类,利用式(1)计算分类后的各区间信息量值,如表2所示。
对逐个栅格单元的信息量值进行叠加计算,继而得到总信息量值,利用自然断点法将总信息量值由低到高划分为极低、低、中、高和极高5个等级,从而得到研究区易发性区划结果,如图2所示。
由图2可知,研究区地质灾害在空间分布上具有明显的差异性特征。湟水流域地质灾害的极高易发区与高易发区主要分布在河谷平原地带及其各级支流中下游两侧,该区域是地质灾害多发区;中易发区主要分布在河谷平原地带及湟水河南北两岸的低山丘陵区;低易发区与极低易发区则主要分布在全流域中高山区。
3.2 结果检验
利用ROC曲线及AUC值对地质灾害易发性评价结果进行精度评定,如图3所示,AUC为0.785,说明易发性评价结果精度较高,该评价结果可靠且易发性区划较为合理,适用于湟水流域地质灾害相关研究。
4 地质灾害气象风险预警模型与预警
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4.1 地质-降雨耦合模型建立
利用2010—2022年湟水流域地质灾害点当日及前期降雨量数据,基于逻辑回归算法构建预警模型。因变量为地质灾害是否发生,若当日地质灾害发生则定义为“1”,反之则定义为“0”。自变量为对应的易发性值、地质灾害发生前每日的降雨量及发生当日降雨量,依据经验将前期降雨量的范围确定为地质灾害发生前1 d、前2 d、前3 d、前4 d、前5 d及前6 d降雨量。
降雨因子选取完毕后添加相同数量的随机点加入模型,采用依据偏似然比检验结果来剔除变量的向后步进计算方法,由于前4 d、前5 d降雨量及前6 d降雨量均未通过显著水平为0.05的Wald检验,予以剔除。剔除完成后重新建模,结果如表3所示。
经过上述步骤得到的结果显示,Nagelkerke R2为0.612,表明模型总体拟合程度较好;通过了显著性检验,各因子间相关性很小,相互独立;经多次拟合,模型对地质灾害发生的预测准确率为84.2%,最终建模结果如式(4)所示。
$\begin{aligned} P= & \exp \left(0.093 I_{1}+0.105 I_{2}+0.090 I_{3}+\right. \\ & \left.0.113 I_{4}+0.574 I_{5}-5.105\right) \times \\ & {\left[1+\exp \left(0.093 I_{1}+0.105 I_{2}+0.090 I_{3}+\right.\right.} \\ & \left.\left.0.113 I_{4}+0.574 I_{5}-5.105\right)\right]^{-1} \end{aligned} $
式(4)中:I1为前3 d降雨量;I2为前2 d降雨量;I3为前1 d降雨量;I4为当日降雨量;I5为地质环境易发性值;P为地质灾害发生概率。
在模型中,易发性值、当日降雨量、前1 d降雨量、前2 d降雨量及前3 d降雨量对灾害的影响均为正值且易发性值在模型中所占的权重最高,之后权重从高到低依次为当日降雨量,前2 d降雨量,前3 d降雨量和前1 d降雨量,表明了前期降雨对灾害的发生有一定的影响。利用经过验证的100条地质灾害点雨量数据进行预警雨量阈值表达式研究。根据表3,选取灾害点发生当日降雨量与前3 d累计降雨量作为评价指标制作散点图,如图4所示。
依据分布情况,作出图中分界线,所有点均分布在界线之上,则预警雨量阈值表达式为
R0=-2.328 7R1-3+35.725
式(5)中:R0为地质灾害发生当日降雨量;R1-3为地质灾害发生前3 d累计降雨量。可以看出,当R0>-2.328 7R1-3+35.725时,有发生地质灾害的可能;而当R0<-2.328 7R1-3+35.725时,发生地质灾害的可能较小。
根据上述表达式,设置预警的触发值V为: V=R0+2.328 7R1-3-35.725。若所有站点V<0,将不必进行地质灾害气象风险预警;若有任意站点V>0,则需要利用建立的地质灾害气象预警模型进行预警。
4.2 模型精度及实例验证
通过两种方法来验证预警模型的精度和合理性。一方面利用ROC曲线与AUC来分析评估预警模型的精确度;另一方面通过研究区历史群发性地质灾害实例验证模型的预报预警能力。
地质-降雨耦合模型构建完成后同样利用ROC曲线及AUC对模型进行精度验证,曲线如图5所示。AUC为0.864,表明逻辑回归模型模拟效果较好并且模型精度满足要求。
2020年湟水流域突发性地质灾害主要集中在汛期6—9月,期间共发生灾害153起,占全年地质灾害发生总数的78.46%,部分典型地质灾害事例情况如表4所示。
由表4可知,2020年8月29日青海省西宁市湟中区拦隆口镇发生2起滑坡灾害,青海省西宁市大通回族土族自治县黄家寨镇大哈门村、上柴村及桦林乡关巴村各发生1起崩塌灾害。以该日发生的5起典型地质灾害为例,结合降雨数据,利用式(4)进行预测,检验模型的预警能力,结果如图6所示。
由图6可知,地质-降雨耦合模型预测的地质灾害高风险区域主要分布在湟水河谷平原地带、一级支流两侧的低山丘陵区及各大支流两侧的构造侵蚀中高山区。基于当日发生的5起典型地质灾害,对地质-降雨耦合模型进行验证,结果发现有4起地质灾害达到一级预警等级,另外1起达到二级预警等级,预警结果与现场实际情况总体吻合,模型可对实际预警工作的开展起到了很好地指导作用。
综上所述,构建的地质-降雨耦合预警模型精度较高并能较合理地划分风险预警等级,提高地质灾害预警准确性,可以为相关部门开展应急决策和防灾减灾工作提供参考。
4.3 湟水流域地质灾害气象风险预警
2024年9月4日前后,受副热带高压东退及北方冷空气的共同影响,青海全省多地出现强降雨天气。预警制作人员结合天气预报及气象站点雨量数据,发现9月5日有站点V>0,利用上述建立的地质灾害气象预警最优模型开展预测和预警工作,得到该日地质灾害预警结果,如图7所示。预警完成后,收集灾情状况,发现9月5日下午,西宁市湟中区鲁沙尔镇红崖沟村发生山体滑坡,位于一级预警风险区内,因前期地质灾害隐患排查到位且转移及时,受威胁区域的28户95位村民全部安全撤离,未造成人员伤亡,是一起成功预警的典型案例。
5 结论
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尽管模型整体预测准确率较高,但由于部分地质灾害发生在人迹罕至、地势复杂的区域,使得样本相对较少且不够全面,而降雨数据的获取受气象站点分布的影响,均会影响预警模型的精确性。地质灾害的发生是多种因素共同作用的结果,选用的因子仅能呈现部分因素的影响,与实际地质及气象环境存在一定差异,在以后的工作中将时刻关注地质及气象环境的动态变化,及时更新,加强对预警模型的校验和完善。得到的主要结论如下。
(1)通过分析湟水流域历史地质灾害的空间分布与地形地貌、地质条件、环境条件、人类工程活动等多因素的统计关系,提出了符合研究区实际的地质灾害易发性评价体系,并基于信息量法得到了准确度较高的易发性区划结果。
(2)研究区地质灾害在空间分布上具有明显的差异性特征。湟水流域地质灾害的极高易发区与高易发区主要分布在河谷平原地带及其各级支流中下游两侧,该区域是地质灾害多发区;中易发区主要分布在河谷平原地带及湟水河南北两岸的低山丘陵区;低易发区与极低易发区则主要分布在全流域中高山区。
(3)基于地质灾害易发性值、当日及前期降雨量数据和逻辑回归方法,建立了具有灾害风险预警能力的区域地质灾害气象风险预警模型,模型的预测准确率达84.2%。以2020年8月29日发生的地质灾害为例,进行地质灾害空间预警效果评价及检验,结果表明本研究构建的预警模型效果好且精度较高,可以提高地质灾害预警准确性。
(4)在湟水流域历史地质灾害雨量统计的基础上获得预警雨量阈值并成功预警红崖沟村山体滑坡。结果表明,模型预警准确性较高,能较合理地划分风险预警等级。
基金
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  • 青海省重点研发与转化计划(2023-SF-122)
文献
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2025年第25卷第21期
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doi: 10.12404/j.issn.1671-1815.2408609
  • 接收时间：2024-11-12
  • 首发时间：2026-01-13
  • 出版时间：2025-07-28
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  • 收稿日期：2024-11-12
  • 修回日期：2025-04-18
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青海省重点研发与转化计划(2023-SF-122)
作者信息
    1 青海大学地质工程学院, 西宁 810016
    2 青海省地理空间信息技术与应用重点实验室, 西宁 810001
    3 武汉大学资源与环境科学学院, 武汉 430079

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* 赵健赟(1981—),男,汉族,甘肃张掖人,博士,副教授,硕士研究生导师。研究方向:对地观测技术与地质资源环境评价。E-mail:。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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