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Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

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Geoscience Frontiers 2021年第6期12卷 243-261页

作者： Zizheng Guo Yu Shi Faming Huang xuanmei fan Jinsong Huang Faculty of Engineering China University of GeosciencesWuhan 430074China School of Civil Engineering and Architecture of Engineering Nanchang UniversityNanchang 330031China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China ARC Centre of Excellence for Geotechnical Science and Engineering University of NewcastleNSW 2287Australia

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility *** study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree *** zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total *** results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

关键词： Landslide susceptibility Frequency ratio C5.0 decision tree K-means cluster Classification Risk management

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Uncertainty pattern in landslide susceptibility prediction modelling:Effects of different landslide boundaries and spatial shape expressions

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Geoscience Frontiers 2022年第2期13卷 62-77页

作者： Faming Huang Jun Yan xuanmei fan Chi Yao Jinsong Huang Wei Chen Haoyuan Hong School of Civil Engineering and Architecture Nanchang UniversityNanchang 330031China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengduSichuan 610059China Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNSWAustralia College of Geology&Environment Xi'an University of Science and TechnologyXi'an 710054China Department of Geography and Regional Research University of ViennaViennaAustria

In some studies on landslide susceptibility mapping(LSM),landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon *** expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution of predicted landslide susceptibility indexes(LSIs);moreover,the presence of irregular landslide boundaries and spatial shapes introduces uncertainties into the *** address this issue by accurately drawing polygonal boundaries based on LSM,the uncertainty patterns of LSM modelling under two different landslide boundaries and spatial shapes,such as landslide points and circles,are *** the research area of Ruijin City in China,a total of 370 landslides with accurate boundary information are obtained,and 10 environmental factors,such as slope and lithology,are ***,correlation analyses between the landslide boundary shapes and selected environmental factors are performed via the frequency ratio(FR)***,a support vector machine(SVM)and random forest(RF)based on landslide points,circles and accurate landslide polygons are constructed as point-,circle-and polygon-based SVM and RF models,respectively,to address ***,the prediction capabilities of the above models are compared by computing their statistical accuracy using receiver operating characteristic analysis,and the uncertainties of the predicted LSIs under the above models are *** results show that using polygonal surfaces with a higher reliability and accuracy to express the landslide boundary and spatial shape can provide a markedly improved LSM accuracy,compared to those based on the points and ***,a higher degree of uncertainty of LSM modelling is present in the expression of points because there are too few grid units acting as model input ***,the expression of the landslide boundary as circl

关键词： Landslide boundary Landslide susceptibility mapping Machine learning Uncertainty analysis Frequency ratio

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The role of earthquake-induced landslides in erosion and weathering from active mountain ranges:Progress and perspectives

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Science China Earth Sciences 2021年第12期64卷 2069-2082页

作者： Zhangdong JIN Robert G.HILTON AJoshua WEST Gen K.LI Fei ZHANG Jin WANG Gaojun LI xuanmei fan Meng-Long HSIEH State Key Laboratory of Loess and Quaternary Geology Institute of Earth EnvironmentChinese Academy of SciencesXi’an710061China Institute of Global Environmental Change Xi’an Jiaotong UniversityXi’an710049China Department of Geography Durham UniversityDurhamDH13LEUK Department of Earth Sciences University of Southern CaliforniaLos AngelesCA90089USA Division of Geological and Planetary Sciences California Institute of TechnologyPasadenaCA91125USA School of Land Engineering Chang’an UniversityXi’an710054China Department of Earth and Planetary Sciences Nanjing UniversityNanjing210023China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu610059China Department of Earth and Environmental Sciences Taiwan Chung Cheng UniversityChiayi600China

Earthquakes play a fundamental role in the evolution of Earth’s topography through co-seismic uplift and subsidence,as well as erosion through widespread landslides induced by ground ***-induced landslides can result in exceptional increases in the transfer of mass from landscapes,supplying sediment to rivers where impacts can last for decades or *** can also erode vegetation and soils,in addition to exposing freshly ground rock mass,which can both result in regionally significant carbon *** work has sought to quantify the fluxes,rates,and temporal patterns of the impacts on earth surface processes generated by these tectonic *** this review,we focus on the impacts of earthquake-induced landslides on erosion and sediment flux,river water chemistry,and carbon export through the lens of the well-studied 2008 Wenchuan *** then discuss these themes in the context of works from the 1999 Taiwan Chi-Chi earthquake,2015 Nepalese Gorkha earthquake,and New Zealand historical earthquakes,altogether highlighting an important role of earthquake-induced landslides in erosion and weathering along tectonically active mountain *** suggest that more monitoring,geochemical tracing,paleo-records,and/or modelling studies are needed to compare the processes and fluxes of catchment erosion and weathering before and after earthquake events,which will help to deepen our understanding not only of the impacts of earthquake-induced landslides on earth surface processes,but also the linkage among high magnitude events,continental erosion and weathering,and the long-term global carbon cycle.

关键词： Earthquake Erosion Chemical weathering The carbon cycle Tectonically active mountain ranges

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Uncertainties in landslide susceptibility prediction modeling:A review on the incompleteness of landslide inventory and its influence rules

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Geoscience Frontiers 2024年第6期15卷 80-104页

作者： Faming Huang Daxiong Mao Shui-Hua Jiang Chuangbing Zhou xuanmei fan Ziqiang Zeng Filippo Catani Changshi Yu Zhilu Chang Jinsong Huang Bingchen Jiang Yijing Li School of Infrastructure Engineering Nanchang UniversityNanchang 330031China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengduSichuan 610059China Science and Technology Department of Jiangxi Province Nanchang 330046China Department of Geosciences University of PadovaPadovaItaly Information Engineering School Nanchang UniversityNanchang 330031China Discipline of Civil Surveying and Conditioning EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNSWAustralia

Landslide inventory is an indispensable output variable of landslide susceptibility prediction(LSP)***,the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been *** Xunwu County,China,as an example,the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions,after which different landslide inventory sample missing conditions are simulated by random *** includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%,20%,30%,40%and 50%,as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in ***,five machine learning models,namely,Random Forest(RF),and Support Vector Machine(SVM),are used to perform ***,the LSP results are evaluated to analyze the LSP uncertainties under various *** addition,this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various *** show that(1)randomly missing landslide inventory samples at certain proportions(10%–50%)may affect the LSP results for local areas.(2)Aggregation of missing landslide inventory samples may cause significant biases in LSP,particularly in areas where samples are missing.(3)When 50%of landslide samples are missing(either randomly or aggregated),the changes in the decision basis of the RF model are mainly manifested in two aspects:first,the importance ranking of environmental factors slightly differs;second,in regard to LSP modelling in the same test grid unit,the weights of individual model factors may drastically vary.

关键词： Landslide susceptibility prediction Landslide inventory Machine learning interpretability SHapley additive explanations Partial dependence plot

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第6次全国青年工程地质学术研讨会综述

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工程地质学报 2017年第5期25卷 1389-1394页

作者：范宣梅地质灾害防治与地质环境保护国家重点实验室(成都理工大学) 成都610059

2017年7月21~23日,第6次全国青年工程地质学术研讨会在成都理工大学地质灾害防治与地质环境保护国家重点实验室顺利召开。本次研讨会是第三届工程地质专业委员会青年工作委员会成立以来组织召开的第二次围绕某一具体主题的学术会议,共... 详细信息

2017年7月21~23日,第6次全国青年工程地质学术研讨会在成都理工大学地质灾害防治与地质环境保护国家重点实验室顺利召开。本次研讨会是第三届工程地质专业委员会青年工作委员会成立以来组织召开的第二次围绕某一具体主题的学术会议,共有来自全国68家单位,包括高校、科研院所和工程单位的185位青年工作者参会。会议结合成都理工大学地质工程学科的研究特色,以"复杂条件下地质灾害形成机理与风险评价"为主题,举行了13场特邀专题报告、10场学术报告、3场圆桌讨论和1天的地震灾区野外考察,报告内容丰富多彩,涉及理论研究、试验研究与工程实践,深度与广度兼备。本次研讨会是国内工程地质及相关领域青年学者开展讨论和深入交流的一次盛会,对推进青年学者之间的学术交流与合作、加强多学科之间的交叉融合创新、促进"产、学、研"结合的成果转化、促进青年人才的成长起到了积极作用。

关键词：青年工程地质地质灾害形成机理风险评价

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植被茂密山区地质灾害遥感解译方法研究

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武汉大学学报（信息科学版） 2020年第11期45卷 1771-1781页

作者：王绚范宣梅杨帆董秀军成都理工大学地质灾害防治与地质环境保护国家重点实验室四川成都610059

受茂密植被覆盖的影响,采用传统的光学遥感影像解译方法无法实现对历史地质灾害的准确识别与精细遥感解译。近年来,激光雷达技术(light detection and ranging,LiDAR)的发展为植被茂密山区地质灾害解译带来了新途径。以九寨沟震区为例,... 详细信息

受茂密植被覆盖的影响,采用传统的光学遥感影像解译方法无法实现对历史地质灾害的准确识别与精细遥感解译。近年来,激光雷达技术(light detection and ranging,LiDAR)的发展为植被茂密山区地质灾害解译带来了新途径。以九寨沟震区为例,基于高精度机载LiDAR数据,采用红色立体地图(red relief image map,RRIM)数据处理方法,避免了数字高程模型(digital elevation model,DEM)山体阴影的不利影响,提出了崩塌、滑坡和泥石流灾害的地貌识别标志与遥感解译方法,提高了植被茂密山区地质灾害遥感解译的精度与准确性。共解译九寨沟震区熊猫海区域震前地质灾害311处,总面积约11.8 km2,在此基础上分析了震前地质灾害的空间分布特征,可以为九寨沟震区地质灾害防治与风险评价提供数据支撑。

关键词：九寨沟地震地质灾害遥感解译激光雷达技术红色立体地图

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板梁状滑坡形成条件、成因机制与防治措施

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岩石力学与工程学报 2010年第2期29卷 242-250页

作者：许强范宣梅李园张帅成都理工大学地质灾害防治与地质环境保护国家重点实验室四川成都610059

以发生于四川省南江县兴马中学滑坡和大河中学滑坡这2个新老滑坡为典型案例,通过详细调查和分析解剖,提出一种新的平推式滑坡类型——板梁状滑坡。板梁状滑坡平面上呈扁长矩形,纵向长度远小于横向宽,剖面上呈薄板状。其主要发生于由近... 详细信息

以发生于四川省南江县兴马中学滑坡和大河中学滑坡这2个新老滑坡为典型案例,通过详细调查和分析解剖,提出一种新的平推式滑坡类型——板梁状滑坡。板梁状滑坡平面上呈扁长矩形,纵向长度远小于横向宽,剖面上呈薄板状。其主要发生于由近水平巨厚层砂岩和薄层泥岩构成的斜坡陡崖附近,坡内存在一组平行于坡面的长大陡倾结构面。在强降雨条件下,平行于坡面的张裂缝被快速充水至某一临界高度后,强大的静水压力将外侧岩体整体平推出去,形成平推式滑坡,而后随着裂缝内水头的骤降,滑体很快会自行止动。在较系统地研究并查明形成板梁状滑坡必须具备的岩性条件、坡体结构条件、裂缝储水条件以及强降雨触发条件的基础上,针对板梁状滑坡的后续变形破坏模式,提出加强截排水,消除在裂缝内形成高水头的条件;在板梁外侧根部采取短桩或抗滑键阻止板梁进一步向外滑动;顶部采用连梁或锚索约束其倾倒变形等有针对性的工程治理措施。

关键词：边坡工程板梁状滑坡平推式滑坡近水平岩层滑坡形成条件成因机制

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基于栅格最大值法的县域综合地质灾害建模

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地质科技通报 2022年第2期41卷 197-208页

作者：范雅婕范宣梅方成勇成都理工大学地质灾害防治与地质环境保护国家重点实验室成都610059

四川省地形高低悬殊,岩性构造发育,各类地质灾害频发,开展地质灾害易发性评价具有重要意义。崩塌、泥石流属于广义上的滑坡,以四川省丹巴县为例,从考虑不同滑坡类别的区域性地质灾害易发性出发综合考虑崩塌、滑坡、泥石流的空间概率分... 详细信息

四川省地形高低悬殊,岩性构造发育,各类地质灾害频发,开展地质灾害易发性评价具有重要意义。崩塌、泥石流属于广义上的滑坡,以四川省丹巴县为例,从考虑不同滑坡类别的区域性地质灾害易发性出发综合考虑崩塌、滑坡、泥石流的空间概率分布。基于ArcGIS通过高精度数字高程模型共选取高程、坡度等10个地质灾害关键控制因素,采用信息量模型对综合地质灾害进行了易发性评价。最终通过ArcGIS的单元统计(Cell Statistics)功能实现多个栅格图层最大值法合成综合易发性,进一步利用受试者工作特征曲线(ROC)验证单种滑坡类别易发性模型的精度。按照自然断点法将研究区划分为极低、低、中、高、极高易发区,高易发区和极高易发区主要集中分布在章谷镇、太平桥乡以及甲居镇等地。研究结果证明信息量模型能对单类地质灾害进行评价,栅格最大值法是获取综合易发性的一种有效评价方法。

关键词：综合地质灾害地理信息系统信息量模型栅格最大值法易发性评价

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基于时间序列与人工蜂群支持向量机的滑坡位移预测研究

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工程地质学报 2019年第4期27卷 880-889页

作者：杨帆许强范宣梅叶微地质灾害防治与地质环境保护国家重点实验室(成都理工大学) 成都610059 四川大学商学院成都610065

总结以往滑坡预测方法存在的诸多不足,针对滑坡监测位移-时间曲线特点,本文提出了一种基于时间序列的人工蜂群算法(ABC)与支持向量回归机(SVR)相结合的滑坡位移预测方法。以三峡库区白水河滑坡为例,通过对滑坡位移、降雨、库水位等因素... 详细信息

总结以往滑坡预测方法存在的诸多不足,针对滑坡监测位移-时间曲线特点,本文提出了一种基于时间序列的人工蜂群算法(ABC)与支持向量回归机(SVR)相结合的滑坡位移预测方法。以三峡库区白水河滑坡为例,通过对滑坡位移、降雨、库水位等因素的分析,研究影响滑坡位移变化的因素。用时间序列加法模型和移动平均法将滑坡位移分解为趋势项和周期项。以多项式最小二乘法拟合滑坡位移趋势项,用人工蜂群支持向量机模型对滑坡位移周期项进行训练和预测。通过灰色系统关联分析法计算多项因子与滑坡位移周期项之间的关联性。最终的滑坡总位移预测值为周期项预测值与趋势项预测值之和。与BP神经网络、PSO-SVR模型方法相比,该方法在滑坡位移预测中有更高的精度,在防灾减灾工作中有较好的推广应用前景。

关键词：滑坡位移预测时间序列人工蜂群算法支持向量回归机

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降雨驱动泥石流危险性评价

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地球科学 2019年第8期44卷 2794-2802页

作者：常鸣窦向阳唐川李宁范宣梅成都理工大学地质灾害防治与地质环境保护国家重点实验室

'5·12'汶川地震后大量滑坡崩塌体出现,伴随极端降雨极易向泥石流转换,其规模及危害程度远高于预期.2010年8月13日都江堰龙池场镇突发暴雨,导致八一沟泥石流暴发,冲毁拦挡坝,掩埋道路、房屋及农田.为了探索降雨驱动泥石流的危险性,选取... 详细信息

'5·12'汶川地震后大量滑坡崩塌体出现,伴随极端降雨极易向泥石流转换,其规模及危害程度远高于预期.2010年8月13日都江堰龙池场镇突发暴雨,导致八一沟泥石流暴发,冲毁拦挡坝,掩埋道路、房屋及农田.为了探索降雨驱动泥石流的危险性,选取八一沟泥石流作为研究对象,通过分析不同降雨频率下的泥石流暴发强度及周期,采用FLO-2D数值模拟方法开展危险性评价.经验证模拟精度可达78%,结合降雨频率(5年、20年、50年、100年、200年)、流速和堆积深度构建八一沟泥石流危险性评价模型并绘制分布图.结果表明,八一沟泥石流危险范围内高危险区占62%,中危险性区占28%,低危险区占10%,该结论为危险范围内重点设施的监测预警提供科学依据.

关键词：八一沟泥石流 FLO-2D 降雨频率危险性评价工程地质

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