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Article(id=1149773876252598287, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2405875, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1722787200000, receivedDateStr=2024-08-05, revisedDate=1739116800000, revisedDateStr=2025-02-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1752057053863, onlineDateStr=2025-07-09, pubDate=1746633600000, pubDateStr=2025-05-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752057053863, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752057053863, creator=13701087609, updateTime=1752057053863, updator=13701087609, issue=Issue{id=1149773869357167407, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='13', pageStart='5273', pageEnd='5704', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752057052207, creator=13701087609, updateTime=1768456769392, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559268744253990, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559268744253991, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=5340, endPage=5350, ext={EN=ArticleExt(id=1149773876487479312, articleId=1149773876252598287, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Susceptibility Assessment of Geological Hazards on Major Traffic Arteries in Western Sichuan Based on Stacking Ensemble Machine Learning Models, columnId=1156262729351549255, journalTitle=Science Technology and Engineering, columnName=Papers·Astronomy and Geosciences, runingTitle=null, highlight=null, articleAbstract=

The terrain in western Sichuan is complex and varied, and the geological structure is active, which makes the construction and maintenance of the traffic trunk line face the challenge of frequent geological disasters. Ensemble learning algorithm can optimize the shortcomings of the algorithm in geological hazard susceptibility assessment and improve the accuracy of the model, which has significant advantages in geological hazard susceptibility assessment. Taking the riverside high-speed as an example, 12 feature variables such as slope and relief were selected to construct the geological hazard susceptibility evaluation system. The forecasting performance of the modeling of the integrated algorithm and a single algorithm was compared and analyzed. The main control factors of the geological disasters along the riverside high-speed were discussed and the practicability of the model was verified. The results show that the proportion of high and extremely high geological hazard prone areas along the Yangtze River high speed is 18.21% and 9.85%, respectively, which are concentrated in the Leibo section and Jinyang section. The area under curve (AUC) of the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve in the integrated model. The AUC of ROC curve (0.84~0.86), the AUC of P-R curve (0.81~0.85) and the F1 score (0.78~0.79) of the three single machine learning models are significantly higher, and the prediction performance is better than that of a single machine learning algorithm. The development of high-speed geological hazards along the Yangtze River is controlled by topographic and geomorphic factors. The new damage points are located in the highly prone areas of the model, which verifies the accuracy and reliability of the Stacking model.

, correspAuthors=Zhi-quan YANG, 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=Feng-tao WU, Zhi-quan YANG, Xu-guang ZHAO), CN=ArticleExt(id=1149773913795813664, articleId=1149773876252598287, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于Stacking集成机器学习模型的川西重大交通干线地质灾害易发性评价, 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=

川西地区地形复杂多变,地质构造活跃,致使交通干线的建设与维护面临着地质灾害频发的挑战。集成学习算法能优化地质灾害易发性评估中算法的不足,提升模型的精度,在地质灾害易发性评估中具有显著优势。以沿江高速为例,选取坡度、起伏度等12个特征变量构建地质灾害易发性评价体系,比较分析Stacking集成算法与单一算法建模的预测性能,探讨了沿江高速地质灾害的主控因素,并验证了模型的实用性。结果表明:沿江高速沿线地质灾害高和极高易发区占比分别为18.21%和9.85%,集中分布在雷波段和金阳段;Stacking集成模型受试者操作特征(receiver operating characteristics,ROC)曲线和精确率-召回率(precision-recall,P-R)曲线的曲线下的面积(area under curve,AUC)均达到0.89,F1分数也达到0.84,显著高于3个单一机器学习模型的ROC曲线的AUC(0.84~0.86)、P-R曲线的AUC(0.81~0.85)和F1分数(0.78~0.79),比单一机器学习算法有更好的预测性能;沿江高速地质灾害的发育受地形地貌因素控制;新增灾害点位于模型中的极高易发区,印证了Stacking模型的精确度和可靠性。

, correspAuthors=杨志全, authorNote=null, correspAuthorsNote=
* 杨志全(1983—),男,汉族,四川平昌人,博士,教授。研究方向:地质灾害形成机理与防治。E-mail:。
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吴逢涛(2000—),男,汉族,云南临沧人,硕士研究生。研究方向:地质灾害形成机理与防治。E-mail:。

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吴逢涛(2000—),男,汉族,云南临沧人,硕士研究生。研究方向:地质灾害形成机理与防治。E-mail:。

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吴逢涛(2000—),男,汉族,云南临沧人,硕士研究生。研究方向:地质灾害形成机理与防治。E-mail:。

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journalId=1146123166801305609, articleId=1149773876252598287, language=CN, label=图11, caption=预测模型验证及实地考察图

(a)Stacking集成模型地质灾害易发性图;(b)RF模型地质灾害易发性图;(c)XGBoost模型地质灾害易发性图;(d)SVM模型地质灾害易发性图;(e)灾害点影像图;(f)灾害点现场拍摄图

, figureFileSmall=HhSJb2XYCyyf1qUoyRSL7g==, figureFileBig=4R7Eq6nY0mZ7DKyQVHgt1g==, tableContent=null), ArticleFig(id=1176923428281008687, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773876252598287, language=EN, label=Table 1, caption=

The evaluation factors selected for the study

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 评价因子释义 数据来源
NDVI NDVI是通过比较近红外和红光反射率来评估植被健康和密度的数值指标 国家青藏高原科学数据中心
DEM 数字高程模型(digital elevation model,DEM),高程指某点沿铅垂线方向到绝对基面的距离 NASA地球科学数据网站
剖面曲率 剖面曲率是指地表在垂直方向上的曲率 基于DEM数据和ArcGIS分析得出
平面曲率 平面曲率是指地表在水平方向上的曲率
起伏度 起伏度是指地表在水平方向上的高程变化程度
坡度 坡度是指地表某一点的坡降
坡向 坡向是地表某一点向下倾斜的方向
岩性 岩性是指岩石的性质和组成特征 中华人民共和国1∶250万数字地质图空间数据库
植被类型 不同类型的植被对地质灾害的影响也有所不同 中国科学院地理科学与资源研究所数据中心
PGA PGA是描述地震强度的重要参数 GB18306—2015附录A:中国地震动峰值加速度区划图
断层距离 断层距离通常指地质学中测量的一个点到断层面的最短距离 中国地理空间数据集
降雨 降雨指的是研究区每年总降雨量平均值 国家青藏高原科学数据中心
), ArticleFig(id=1176923428343923248, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773876252598287, language=CN, label=表1, caption=

研究选取的评价因子

, figureFileSmall=null, figureFileBig=null, tableContent=
评价因子 评价因子释义 数据来源
NDVI NDVI是通过比较近红外和红光反射率来评估植被健康和密度的数值指标 国家青藏高原科学数据中心
DEM 数字高程模型(digital elevation model,DEM),高程指某点沿铅垂线方向到绝对基面的距离 NASA地球科学数据网站
剖面曲率 剖面曲率是指地表在垂直方向上的曲率 基于DEM数据和ArcGIS分析得出
平面曲率 平面曲率是指地表在水平方向上的曲率
起伏度 起伏度是指地表在水平方向上的高程变化程度
坡度 坡度是指地表某一点的坡降
坡向 坡向是地表某一点向下倾斜的方向
岩性 岩性是指岩石的性质和组成特征 中华人民共和国1∶250万数字地质图空间数据库
植被类型 不同类型的植被对地质灾害的影响也有所不同 中国科学院地理科学与资源研究所数据中心
PGA PGA是描述地震强度的重要参数 GB18306—2015附录A:中国地震动峰值加速度区划图
断层距离 断层距离通常指地质学中测量的一个点到断层面的最短距离 中国地理空间数据集
降雨 降雨指的是研究区每年总降雨量平均值 国家青藏高原科学数据中心
), ArticleFig(id=1176923428419420721, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773876252598287, language=EN, label=Table 2, caption=

Partition statistics of susceptibility zoning

, figureFileSmall=null, figureFileBig=null, tableContent=
分区等级 Stacking集成 RF SVM XGBoost
面积比例/% 灾害点数/个 面积比例/% 灾害点数/个 面积比例/% 灾害点数/个 面积比例/% 灾害点数/个
极低 4.81 22 14.67 89 15.53 1 16.45 116
低 34.15 81 32.19 134 30.68 110 21.76 304
中 32.98 165 25.15 234 36.93 310 25.72 239
高 18.21 378 20.07 260 15.20 415 22.18 165
极高 9.85 294 7.92 223 1.67 104 13.89 116
), ArticleFig(id=1176923428486529586, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773876252598287, language=CN, label=表2, caption=

易发性分区统计结果

, figureFileSmall=null, figureFileBig=null, tableContent=
分区等级 Stacking集成 RF SVM XGBoost
面积比例/% 灾害点数/个 面积比例/% 灾害点数/个 面积比例/% 灾害点数/个 面积比例/% 灾害点数/个
极低 4.81 22 14.67 89 15.53 1 16.45 116
低 34.15 81 32.19 134 30.68 110 21.76 304
中 32.98 165 25.15 234 36.93 310 25.72 239
高 18.21 378 20.07 260 15.20 415 22.18 165
极高 9.85 294 7.92 223 1.67 104 13.89 116
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基于Stacking集成机器学习模型的川西重大交通干线地质灾害易发性评价
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吴逢涛 , 杨志全 * , 赵旭光
科学技术与工程 | 论文·天文学、地球科学 2025,25(13): 5340-5350
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科学技术与工程 | 论文·天文学、地球科学 2025, 25(13): 5340-5350
基于Stacking集成机器学习模型的川西重大交通干线地质灾害易发性评价
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吴逢涛 , 杨志全* , 赵旭光
作者信息
  • 昆明理工大学公共安全与应急管理学院, 昆明 650093

    • 吴逢涛(2000—),男,汉族,云南临沧人,硕士研究生。研究方向:地质灾害形成机理与防治。E-mail:。

通讯作者:

* 杨志全(1983—),男,汉族,四川平昌人,博士,教授。研究方向:地质灾害形成机理与防治。E-mail:。
Susceptibility Assessment of Geological Hazards on Major Traffic Arteries in Western Sichuan Based on Stacking Ensemble Machine Learning Models
Feng-tao WU , Zhi-quan YANG* , Xu-guang ZHAO
Affiliations
  • Faculty of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming 650093, China
出版时间: 2025-05-08 doi: 10.12404/j.issn.1671-1815.2405875
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摘要
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川西地区地形复杂多变,地质构造活跃,致使交通干线的建设与维护面临着地质灾害频发的挑战。集成学习算法能优化地质灾害易发性评估中算法的不足,提升模型的精度,在地质灾害易发性评估中具有显著优势。以沿江高速为例,选取坡度、起伏度等12个特征变量构建地质灾害易发性评价体系,比较分析Stacking集成算法与单一算法建模的预测性能,探讨了沿江高速地质灾害的主控因素,并验证了模型的实用性。结果表明:沿江高速沿线地质灾害高和极高易发区占比分别为18.21%和9.85%,集中分布在雷波段和金阳段;Stacking集成模型受试者操作特征(receiver operating characteristics,ROC)曲线和精确率-召回率(precision-recall,P-R)曲线的曲线下的面积(area under curve,AUC)均达到0.89,F1分数也达到0.84,显著高于3个单一机器学习模型的ROC曲线的AUC(0.84~0.86)、P-R曲线的AUC(0.81~0.85)和F1分数(0.78~0.79),比单一机器学习算法有更好的预测性能;沿江高速地质灾害的发育受地形地貌因素控制;新增灾害点位于模型中的极高易发区,印证了Stacking模型的精确度和可靠性。

地质灾害  /  机器学习  /  易发性评价  /  高速公路  /  预测精度

The terrain in western Sichuan is complex and varied, and the geological structure is active, which makes the construction and maintenance of the traffic trunk line face the challenge of frequent geological disasters. Ensemble learning algorithm can optimize the shortcomings of the algorithm in geological hazard susceptibility assessment and improve the accuracy of the model, which has significant advantages in geological hazard susceptibility assessment. Taking the riverside high-speed as an example, 12 feature variables such as slope and relief were selected to construct the geological hazard susceptibility evaluation system. The forecasting performance of the modeling of the integrated algorithm and a single algorithm was compared and analyzed. The main control factors of the geological disasters along the riverside high-speed were discussed and the practicability of the model was verified. The results show that the proportion of high and extremely high geological hazard prone areas along the Yangtze River high speed is 18.21% and 9.85%, respectively, which are concentrated in the Leibo section and Jinyang section. The area under curve (AUC) of the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve in the integrated model. The AUC of ROC curve (0.84~0.86), the AUC of P-R curve (0.81~0.85) and the F1 score (0.78~0.79) of the three single machine learning models are significantly higher, and the prediction performance is better than that of a single machine learning algorithm. The development of high-speed geological hazards along the Yangtze River is controlled by topographic and geomorphic factors. The new damage points are located in the highly prone areas of the model, which verifies the accuracy and reliability of the Stacking model.

geohazards  /  machine learning  /  susceptibility evaluation  /  expressway  /  prediction accuracy
吴逢涛, 杨志全, 赵旭光. 基于Stacking集成机器学习模型的川西重大交通干线地质灾害易发性评价. 科学技术与工程, 2025 , 25 (13) : 5340 -5350 . DOI: 10.12404/j.issn.1671-1815.2405875
Feng-tao WU, Zhi-quan YANG, Xu-guang ZHAO. Susceptibility Assessment of Geological Hazards on Major Traffic Arteries in Western Sichuan Based on Stacking Ensemble Machine Learning Models[J]. Science Technology and Engineering, 2025 , 25 (13) : 5340 -5350 . DOI: 10.12404/j.issn.1671-1815.2405875
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地质灾害的形成与触发是一个多因素、多层次的复杂过程,这些因素通常被分为内生因素(地形地貌、地质构造及植被覆盖等)和外发诱因(降雨事件、地震活动以及人类的工程活动等)两大类[1]。这些因素在时间和空间上的复杂交互作用,增加了灾害发生的不确定性和复杂性,给地质灾害易发性评价带来了巨大的挑战。随着中国西部大开发战略的持续推进,川西地区作为战略重要组成及实施地区,区域内泥石流、滑坡和崩塌等地质灾害频发,严重威胁着周边交通安全和区域经济可持续发展[2-4]。因此,亟须选取一种有效方法合理量化评估和预测地质灾害的潜在危险区域,以降低地质灾害对交通干线建设与维护带来的影响,减少人类工程活动在与自然环境竞争中的损失[5]。
20世纪以来,遥感信息技术发展迅速,基于遥感数据的地质灾害评价方法为此类研究提供了新的途径。众多学者尝试利用地理信息系统(geographic information system,GIS)结合经验及统计方法,如模糊数学[6]、地理加权[7]、层次分析[8]等方法对地质灾害易发性评价开展了大量研究工作,有效地处理和分析大量的空间数据,集成多维数据源,包括遥感影像、地形地质数据及历史灾害记录,从而提供一个直观、多角度的灾害风险评估。但上述依靠经验统计或主观判断的评价方法,在地质影响因素较为复杂的区域,难以满足更准确有效的评价需求。近年来,随着计算机科学技术的快速发展,随机森林(random forest,RF)[9-11]、神经网络[12]、支持向量机(support vector machine,SVM)[13]、极端梯度提升(extreme gradient boosting,XGBoost)[14]、Logistics[15]等这类机器学习、深度学习的智能科学方法开始被广泛地应用于地质灾害的评价之中[16]。很大程度上提升了易发性评价模型的准确程度。Zhao等[17]通过对比RF、SVM和反向传播神经网络模型对中国横断山区地质灾害进行了易发性评估,最终认为RF模型展现了最高的预测性能;Ahmad等[18]通过不同模型对印度河盆地的地质灾害进行评估和易发性制图,对比验证发现逻辑回归(logistic regression,LR)模型在评估上印度河盆地地区的地质灾害易发性方面效果最好。但是,相同模型受不同地质环境和数据背景的影响,预测精度可能存在显著差异,哪种模型在易发性评价中性能更好仍没有统一的结论[19]。当前大部分地质灾害易发性评价主要依赖于单一机器学习模型的应用,但这些模型容易受到数据缺陷的影响[20],限制评估结果的准确性。为克服这一限制,集成算法成为一种有效的选择。集成算法能够整合多个独立算法来训练模型,
避免了选择合适机器学习算法的难题,并且能够处理复杂和高维数据,从而获得比单一算法更高精度的结果[21];Stacking是一种典型的序列式集成学习算法,结合不同模型的优点,采用交叉验证来避免模型在同一数据上进行训练和预测,避免模型过拟合,最终提高整体的预测准确性和鲁棒性。
现以地质灾害多发的G4216高速公路沿线区域作为研究区,利用RF、XGBoost和SVM这3 种机器学习模型,通过Stacking集成的方法对研究区域内的地质灾害易发性进行对比分析,旨在确定最适用于该区域的模型及测试方法的评价精度。以期为川西高速公路的防灾减灾工作和工程建设提供重要的参考和指导。
1 研究区概况
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G4216线(也称沿江高速)位于四川省凉山彝族自治州与宜宾市之间,呈东北至西南的走向,东起宜宾市屏山县,西至凉山彝族自治州宁南县,全长262.1 km,如图1所示。研究区呈现出独特的金沙江高山峡谷气候,干湿季节分明[22],年降雨量集中在6—9月。短期的丰富降雨为地质灾害形成提供了充足的水动力。同时,区内地质构造复杂,新构造活动频繁,受到金沙江及其支沟的深刻影响,岩体在卸荷过程中引发的表生构造裂隙呈现出极为显著的发育状态,为地质灾害提供了良好的形成基础。
近年来,随着该区域对道路建设和资源开发活动的加强,天然生态环境遭受强烈工程外部扰动,加剧了地质环境的脆弱性,导致当地生态平衡受到破坏。上述人类活动为泥石流、滑坡、崩塌等地质灾害频发创造条件,不仅提高了灾害发生的概率,还对人类生活和区域发展构成了严重威胁。
2 数据来源和研究方法
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研究中涉及的数据、计算步骤和方法的工作流程如图2所示,主要包括以下4个阶段。
(1)在实地调查的基础上利用卫星遥感影像进行解译,建立地质灾害数据库。
(2)对地质灾害相关评价因子进行共线性分析。
(3)使用机器学习算法训练构建地质灾害易发性模型。
(4)对比验证Stacking集成机器学习模型与单一机器学习模型之间的性能差异。
2.1 数据来源
2.1.1 地质灾害数据库
用于进行易发性建模的地质灾害点数共有 940 个,其中包括泥石流灾害159 个、滑坡灾害404 个和崩塌灾害377 个,主要通过以下3种途径获得。
(1)沿江高速各标段项目部在开展高速公路建设前期对周围区域进行的地质灾害点位调查。
(2)通过Google Earth影像识别地质灾害点,将流域的Melton Ratio值大于0.6、堆积扇大于3.3°作为区别泥石流和洪水沟的最小阈值[23];通过滑坡的滑动轨迹和明显的前缘后缘特征识别滑坡点;崩塌灾
害区域坡面的裸露岩石或土体颜色与周围环境明显不同,也有坠落痕迹,但因其易与滑坡识别相重合,多以实地调查点位为主。
(3)由于部分区域的影像精度不足,无法准确识别,在2023年5月和2023年9月进行了实地考察,验证核实遥感解译的准确性,并调查是否有新增灾害点(图3)。
通常情况下,滑坡和灾害在空间上可以被简化成一个点,但是泥石流是一个流域过程,因此,为更好地体现泥石流灾害影响因素的完整性,选取每个评价分级在泥石流流域内栅格的均值作为其特征值。将发生地质灾害的区域标记为1,未发生过地质灾害的区域标记为0,并随机在研究区域内选取了相同数量的非地质灾害点[24](940 个非地质灾害点)。通常训练集与测试集相对比率大小并没有严格的限制[25],因此将70%的数据作为训练集、30%数据作为测试集,训练数据集和测试数据集都包含比例为1∶1的正样本和负样本。
2.1.2 评价因子选取
地质灾害的发生涉及地形地貌、气象环境、地质构造等多种因素影响,共选取12 个评价因子,包括坡度、起伏度、平面曲率、剖面曲率、坡向、高程、地震峰值加速度(peak ground acceleration,PGA)、降雨、断层距离、归一化差分植被指数(normalized difference vegetation index,NDVI)、岩性和植被类型(图4)。评价因子具体物理意义和数据来源见表1。
在进行地质灾害易发性评价的过程中,评价因子的选择及其相互关系对模型的准确性和稳定性具有决定性影响。评价因子之间的共线性过强,会导致模型无法准确分析评价因子与地质灾害之间的实际关系,影响易发性分析的准确度。因此,有必要在地质灾害易发性建模时对评价因子之间进行共线性检验。利用皮尔逊相关系数和方差膨胀因子(variance inflation factor, VIF)来确定各评价因子之间是否存在高共线性关系,以确保模型的准确性。大部分研究表明[26-29],当皮尔逊系数<0.7,方差膨胀系数(VIF)<5,被认为是共线性关系不强,能够用于模型的构建。
2.2 研究方法
2.2.1 Stacking集成模型
Stacking集成模型是一种机器学习集成技术,通过结合多个不同模型的预测能力以提高整体性能[30]。此技术主要由两个层次的模型构成:基模型和元模型。其核心理念在于,将多个初级学习器的预测结果转化为新的特征变量,进而利用这些新特征来训练次级学习器,以产生最终的预测结果。
RF、XGBoost和SVM作为基模型,可以结合它们在处理多样性、非线性数据和稀疏数据方面的优势,提高整体模型的精确性,逻辑回归作为元模型,使其能够有效结合不同基础模型的预测结果,提高整体模型的性能和泛化能力。通常在进行数据集的分割和训练验证时多采用五折交叉验证,但由于验证集较大,训练集较小,会导致模型的精度误差较大,而六折交叉法可以使模型的验证集减少,训练集增加,提供更稳定和更精确的模型。因此,采用六折交叉验证来进行数据集的分割和训练验证。
2.2.2 RF模型
RF是一种通过构建多个决策树并汇总其预测结果来提高预测精度的集成学习方法[31]。在地质灾害评价中,RF能够处理包括地形、地质构造、气象水文在内的多种类型数据,以评估地质灾害风险。该模型通过随机选择样本和特征来增加多样性,有效减少过拟合风险,在地质灾害评价领域方面获得较多成功应用[32-33]。
2.2.3 XGBoost模型
XGBoost是一种基于决策树的集成学习算法[34],主要用于分类和回归问题。在地质灾害评价中,XGBoost通过分析地形、地质以及气象等多种因素的数据,逐步添加决策树以预测地质灾害风险,并持续优化模型以减少预测误差,提高地质灾害评价的准确度和可靠性。
2.2.4 SVM模型
SVM是一种适用于分类和回归问题的监督学习算法[21]。在地质灾害评价中,SVM可以有效地处理线性和非线性关系,在处理高维数据和小样本问题时表现较好。
2.3 模型精度评估
在地质灾害易发性建模中,评估模型性能必不可少[20]。采用混淆矩阵及其相关的参数验证不同算法预测地质灾害易发性的合理性。混淆矩阵包含了模型的4种可能结果:真阳性(TP)、真阴性(TN)、假阳性(FP)和假阴性(FN)。其中,TP和FP分别表示正确分类的地质灾害样本数和错误分类为地质灾害的样本数,而TN和FN分别表示正确分类的非地质灾害样本数和错误分类为非地质灾害的样本数。受试者工作特征曲线(receiver operating characteristics,ROC)已被广泛用于评估和验证易发性预测模型的性能。曲线下面积(area under curve,AUC)是衡量模型精度的定量指标,通常AUC越高,模型的性能越好;AUC越接近1,模型越精确。AUC一般被分为5个等级:差、平均、好、非常好和优秀,对应区间分别为0.5~0.6、0.6~0.7、0.7~0.8、0.8~0.9和0.9~1.0[35]。同时,为了对模型的性能进行更为准确的评价,将精度(precision,P)、召回率(recall,R)和F1分数作为模型性能指标,精度反映正确预测为正类的样本比例,召回率表示模型识别正类样本的能力,而F1分数则综合了精度和召回率,平衡了它们之间的关系。
P= T P T P + F P
R= T P T P + F N
F1= 2 T P 2 T P + F P + F N
3 地质灾害易发性评价结果
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3.1 共线性检验结果
图5显示了12个评价因子之间的泊松相关系数,各指标因子之间未出现较高的相关性,呈弱相关或不相关。其中,PGA与降雨、坡度和起伏度的相关系数为相对较高,分别为0.68和0.61,其原因可能为地震高发的地区,在降雨的影响下很容易有地质灾害的生成,一般降雨和地震的共同作用是引发地质灾害的关键,因此认为二者之间的相关性较高。而坡度和起伏度都反映了地形的陡峭程度和高度变化,因此高度相关。其余系数都在0.42以下,关联性不强。
同时从图6中可以看出,所有评价因子的方差膨胀因子VIF都在1.02~2.46,满足VIF<5的不共线阈值要求。结合两个分析结果可知,所选取的12个评价因子均可用于模型的训练和验证。
3.2 地质灾害易发性
将4种算法得到的易发性结果利用ArcGIS中的自然间断法划分为5 个等级,分别为极低、低、中、高和极高(图7)。从表2可以看出,不同模型表现出显著差异,具体来说,XGBoost模型预测的极高易发区占比最高,达到13.89%,而Stacking集成模型的相应占比仅为9.85%。但通过实际灾害点的易发区分布对比分析,Stacking集成模型展示出更高的精度,其中71.49%的灾害点位于高易发和极高易发区,而RF、SVM和XGBoost模型的相应比例分别为51.38%、55.21%和29.89%。尽管XGBoost模型划定了较多的高风险区域,其实际预测效果并不理想,未能准确标定多数地质灾害的发生区域。相比之下,Stacking集成模型虽然标定的高风险区域较少,但其预测准确性更高。
图8显示了3种单一机器学习模型和Stacking集成模型的ROC曲线、P-R曲线和F1分数曲线。RF、SVM和XGBoost模型ROC曲线的AUC分别为0.86、0.84和0.86;P-R曲线的AUC分别为0.85、0.81和0.82;F1分别为0.78、0.78和0.79。而Stacking集成模型ROC曲线的AUC、P-R曲线的AUC和F1分别为0.89、0.89和0.84,表现最佳,与单一模型相比,ROC曲线的AUC相较RF、SVM和XGBoost模型分别提高了3.3%、5.6%和3.3%;P-R曲线的AUC分别提高了4.5%、9.0%和7.9%;F1分别提高了7.1%、7.1%和6.0%。其结果表明3种单一模型的预测能力相近,但 Stacking 集成模型在各项评估指标上均表现出显著优势,尤其是在保证较高召回率的同时显著提高了精确率,体现了更优的综合预测能力。
4 结果分析
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4.1 地质灾害的主控因素
造成不同类型的地质灾害的影响因素存在明显的不同。因泥石流与滑坡崩塌的形成条件具有显著的差异性,因此将它们划分为两种类型,构建不同的堆叠模型进行主控因素分析。结果显示,高程、平面曲率和岩性是影响泥石流发育的主要影响因素,NDVI、高程和地形起伏度是影响滑坡崩塌发育的主要影响因素(图9),地形地貌是造成泥石流、滑坡和崩塌发育的根本因素。
地质灾害多集中在金阳段,通过金阳县地形地貌特征对地质灾害主控因素进行分析。金阳段具有山高谷深、地势陡峭的地理环境(图10)。谷缘到谷底的较大落差使得山体具有很大的潜在能量。“V”形沟谷加剧了水流的集中与流速,促进了泥石流扇或冲洪堆积地貌的频繁形成。同时,高陡斜坡的前缘卸荷和应力重分布,导致区域应力变化复杂,容易形成长而深的张裂缝,逐渐演化成连续贯通的分离面,增加坡面的失稳概率。而地形的陡峭性不仅增大了坡面的重力势能,还使得下坡水流和侵蚀材料的运动加速,进一步削弱坡面的稳定性,为滑坡和崩塌的形成提供了基础。
4.2 灾害案例验证分析
根据新增实际地质灾害点,对本文模型预测能力的直接验证。该滑坡位于宁南县跑马镇,具有典型的滑坡地质特征和明确的空间位置,地理坐标(27°14'49.60″N,102°52'42.07″E)。据此,进一步对4种模型的预测能力进行了评估。图11结果显示,Stacking集成和SVM模型成功预测该点为极高易发区,而RF和XGBoost模型则将其预测为高易发区。这一对比验证了Stacking集成模型在地质灾害易发性预测中的优越性,尤其是其在精度和可靠性方面的表现。
4.3 模型适用性分析
Stacking集成机器学习模型作为前瞻性的技术展现了更快的训练运算速度,其参数空间组合和调整更加合理,更适用于研究区的地质灾害易发性评估。但在样本选取上,随机采样法是在未发生地质灾害的区域内随机选取非地质灾害点,该方法选取的样本可能与地质灾害发育区的地质环境背景相似,影响易发性建模的精度,因此,还应找寻更为普适有效的非地质灾害取样的约束方法,使模型更为精确可靠。
5 结论
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采用Stacking集成算法评估沿江高速地质灾害易发性,比较集成机器学习模型与单一机器学习模型的性能,并结合Stacking集成算法对新增灾害点进行实际应用。
(1)Stacking集成算法结果显示,沿江高速沿线地质灾害极低、低、中、高和极高易发区面积占比分别为4.81%、34.15%、32.98%、18.21%和9.85%。高和极高地质灾害易发区域集中在沿江高速金阳段。
(2)Stacking集成算法与单一机器学习算法均在地质灾害易发性评价中表现优异,特别是Stacking继承算法在预测精度和灾害点识别方面都有较高的性能表现,ROC曲线和P-R曲线的AUC均达到0.89,F1分数值也达到0.84。在维持高召回率的同时,也实现了高精确度,显著优于3个单一机器学习模型。
(3)沿江高速地质灾害的形成主要受地形地貌因素控制。
(4)通过野外实地考察的新增灾害点验证了Stacking集成模型在实际应用中的有效性。证明该模型能够在沿江高速区域较准确地预测极高易发区,能为该区域公路工程地质灾害预防提供帮助。
基金
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  • 云南省基础研究计划(202501AS070124)
  • 云南省“兴滇英才支持计划”产业创新人才专项(yfgrc202408)
  • 云南省基础研究专项(202401AV070010)
  • 国家自然科学基金(41861134008)
  • 国家重点研发计划(2023YFC3008301)
  • 四川省自然科学基金(2023NSFSC2086)
  • 云南省基础研究计划总计划(202001AT070043)
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2025年第25卷第13期
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doi: 10.12404/j.issn.1671-1815.2405875
  • 接收时间：2024-08-05
  • 首发时间：2025-07-09
  • 出版时间：2025-05-08
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  • 收稿日期：2024-08-05
  • 修回日期：2025-02-10
基金
云南省基础研究计划(202501AS070124)
云南省“兴滇英才支持计划”产业创新人才专项(yfgrc202408)
云南省基础研究专项(202401AV070010)
国家自然科学基金(41861134008)
国家重点研发计划(2023YFC3008301)
四川省自然科学基金(2023NSFSC2086)
云南省基础研究计划总计划(202001AT070043)
作者信息
    昆明理工大学公共安全与应急管理学院, 昆明 650093

通讯作者:

* 杨志全(1983—),男,汉族,四川平昌人,博士,教授。研究方向:地质灾害形成机理与防治。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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