Risk Assessment of Secondary Geological Disasters Induced by the Yushu Earthquake

The Yushu Ms 7.1 earthquake occurred on April 14,2010 in Qinghai Province,China.It induced a mass of secondary geological disasters,such as collapses,landslides,and debris flows.Risk assessment maps are important for geological disaster prevention and mitigation,and also can serve as a guide for post-earthquake reconstruction.Firstly,a hazard assessment index system of secondary geological disasters in the earthquake region was built in this paper,which was based on detailed analysis of environmental and triggering factors closely related to geological disasters in the study area.GIS technology was utilized to extract and analyze the assessment index.Hazard assessment maps of secondary geological disasters were obtained by spatial modeling and overlaying analysis.Secondly,an analysis of the vulnerability of hazard bearing bodies in the area was conducted,important information,such as, population density,percentage of arable land, industrial and agricultural outputs per unit area were regarded as assessment indices to evaluate socioeconomic vulnerability.Thirdly,the risk level of secondary geological disasters of the area was obtained by the formula:Risk=Hazard×Vulnerability. Risk assessment maps were categorized into four levels,including"low","moderate","high"and"very high".These results show that some urban areas are at very high risk,including Jiegu,Chengwen,Xiaxiula and Sahuteng towns.This research can provide some references and suggestions to improve decisionmaking support for emergency relief and post- earthquake reconstruction in the study area.

Study on Quantitative Assessment for the Social Risk of Major Geological Disasters

Based on the analysis of social risk of geological disasters,the index system of social risk evaluation was established. To assess the social risk quantitatively,a quantitative evaluation model of the social risk was established based on AHP,and the social risk of geological disasters was graded. Finally,the evaluation model was applied in a case.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

Application of oblique photogrammetry technique in geological hazard identification and decision management

With the continuous development of the oblique photography technique, it has been used more and more widely in the field of geological disasters. It can quickly obtain the three-dimensional(3D) real scene model of dangerous mountainous areas under the premise of ensuring the safety of personnel while restoring the real geographic information as much as possible. However, geological disaster areas are often accompanied by many adverse factors such as cliffs and dense vegetation. Based on this, the paper introduced the flight line design of oblique photogrammetry, analyzed the multi-platform data fusion processing, studied the multi-period data dynamic evaluation technology and proposed the application methods of data acquisition, early warning, disaster assessment and decision management suitable for geological disaster identification through the analysis of actual cases, which will help geologists to plan and control geological work more scientifically and rationally, improve work efficiency and reduce the potential personnel safety hazards in the process of geological survey, to offer technical support to the application of oblique photogrammetry in geological disaster identification and decision making and provide the scientific basis for personal and property safety protection and later-stage geological disaster management in disaster areas.

Risk Assessment of Coal Mine Water Hazard Based on Three-Dimensional Model of Geology and Underground Space

Mine safety have top-five disasters,which including the water,gas,fire,dust and geological dynamic disaster.The coal mine water disaster is one of the important factors which restricted the development of China’s coal production.It is showed by statistics that 60%of mine accidents are affected by groundwater,which not only result in the production losses,casualties and a variety of

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

Research on Risk Assessment of City Natural Disaster based on Neural Network

Aiming at the selection of fuzzy AHP and fuzzy DH methods in the previous studies, this paper evaluate the qualitative index system using expert questionnaire, the self-learning BP neural network model to construct the index of system, and complete the establishment of model, in order to avoid the serious subjectivity, and using statistical and measurement methods test the reliability index, analyze the validity of the evaluation index system and completeness. Finally, the paper validate the practicability of the model.

小尺度山区地质灾害隐患的无人机精细化识别方法与实践

本文针对小空间尺度范围内以滑坡、崩塌为主的山区地质灾害,提出了一套基于小型无人机摄影测量的精细化隐患识别方法。首先,针对工作区开展至少两期无人机摄影测量作业,经处理后得到实景三维模型、数字正射影像(DOM)、数字表面模型(DSM)等精细化成果;其次,以两期DOM与DSM变化检测为主实现灾害体识别;然后,基于灾害体共性特征建立典型识别标志,并依此采用三维实景目视解译为主方法实现孕灾体识别;最后,通过地面核查确认或排除隐患。将该套方法应用到三峡库首秭归泄滩河左岸顺向斜坡区域,共识别出10处不同类型隐患,证明了方法的可行性。

不同降雨工况条件下的崩滑地质灾害危险性评价

为针对性地采取预防、避让、治理等地质灾害防治与管控提供依据,完善在地质灾害危险性评价中将降雨作为单一诱发因子参与评价体系的弊端,在大雨、暴雨、大暴雨和特大暴雨4种不同降雨工况条件下进行了研究区崩滑地质灾害危险性评价。以云南省元阳县作为研究区域,以栅格单元作为评价单元,选取地貌类型、高程、坡度、坡向、曲率、工程地质岩组、土地利用类型、断层距离和河流距离9个评价因子,采用主观的层次分析法与客观的信息量模型相结合的加权信息量模型对元阳县崩塌、滑坡进行了地质灾害易发性评价。研究结果表明:基于自然间断点法元阳县域可分为低、中、高、极高4类易发区,4类易发区面积分别占元阳县面积的21.55%,35.46%,25.53%和17.16%。利用ROC曲线得出区划结果精度AUC值为0.812,表明区划结果很好。在易发性评价基础上,以年平均最大日降雨量为诱发因素,分别对大雨、暴雨、大暴雨和特大暴雨4种工况条件下的研究区进行了崩塌、滑坡地质灾害危险性评价,得到了大雨([25,50) mm)工况、暴雨([50,100) mm)工况、大暴雨([100,250]mm)工况和特大暴雨(>250 mm)工况4种条件下基于极值假设的研究区崩滑地质灾害危险性评价结果,并对不同降雨工况条件下的地质灾害危险性评价结果进行了对比分析,确定了地质灾害危险性评价结果在不同降雨条件下的空间合理性。通过与实际调查结果的对比表明,4种不同降雨工况条件下的崩滑地质灾害危险性评价结果与实际高度吻合,说明评价结果具有较高的可靠性与合理性。

铁路地质灾害防控面临的挑战与对策

铁路地质灾害防控是铁路安全保障工作的重中之重。在深入分析铁路地质灾害防控主要影响因素的基础上,总结铁路地质灾害防控面临的四大挑战:高海拔高山峡谷区地质勘察难度大、地质灾害准确识别评估难度大、地质灾害精准监测预警难度大、复杂大型地质灾害防治难度大,提出强化地质勘察深度、开展地质灾害识别评估、积极应用监测预警新技术、不断创新地质灾害防治技术、加强地质灾害风险管控等5项防控对策,并分析铁路地质灾害防治的发展方向,为确保铁路行车安全、推动铁路高质量发展奠定坚实基础。

地震诱发煤矿次生灾害类型与评估方法

为解决煤矿承灾体受地震灾害影响易发生次生灾害风险的问题,首先,调查和分析典型案例,归纳总结煤矿次生灾害类型与破坏特征,并基于人、机、环、管全要素,构建地震诱发煤矿次生灾害风险评估三级指标体系;然后,提出风险等级和隐患重要度评估方法;最后,以河北省某市煤矿为例开展实际应用。结果表明:地震诱发煤矿次生灾害类型按照承灾体分为6类;地震诱发煤矿次生灾害发生可能性评估主要考虑煤矿所在区域地震灾害等级、煤矿次生灾害危险性、煤层瓦斯赋存致灾性、矿井水文地质条件致灾性、承灾体设防水平、防灾减灾能力6方面,后果严重性评估主要考虑灾害可能造成的伤亡人数和经济损失2方面;该市煤矿抗震设防薄弱环节为通风系统、井筒支护系统、排水系统,应有针对性地提高抗震设防等级。

基于多源模型的粤北山区县域地质灾害危险性评价与驱动力分析

地质灾害危险性评价对降低地质灾害发生的不确定性及区域防灾减灾至关重要。本研究以粤北山区翁源县为例,选取与地质灾害密切相关的自然环境、区位距离与人类活动方面共13个评价指标,并基于皮尔逊相关系数进行评价指标相关性验证。采用信息量-随机森林耦合模型(Information-Random forest model,I-RF)对研究区地质灾害危险性进行评价,并结合地理探测器(Geodetector,GD)对地质灾害危险性驱动因子进行分析。研究结果显示:ROC曲线验证AUC值达到0.890,表明I-RF耦合模型可以较好地分析刻画研究区地质灾害危险性格局;研究区较高、高易发区主要分布河谷平原区和丘陵区,占比49.85%,中低易发区主要分布低山丘陵区,占比50.15%。县域中心龙仙镇遭受地质灾害的危险性较大,铁龙镇地质灾害危险性较低;因子探测结果显示高程、人口足迹和地质岩组是研究区崩塌、滑坡和泥石流三种主要地质灾害共有的诱发因子,交互探测结果显示高程是研究区地质灾害发生首要驱动因子,崩塌和滑坡主要受高程与距河流距离、地形起伏度和暴雨雨量交互驱动影响,泥石流则主要受高程与地形地貌、地质构造、水文气象和人类活动等方面评价指标交互驱动影响。总体来看,研究区地质灾害危险程度较高,应针对不同的地质灾害危险区和地灾类型,实施差异化的地质灾害防治措施。

基于I+LR方法及GIS平台的麻城市地质灾害建模

随着城镇化建设的推进,频发的山地地质灾害不仅制约了山区集镇的发展,还可能造成重大的人员伤亡和财产损失,因此进行地质灾害易发性研究对区域地质灾害的预测、防治工作具有重要意义。为提高山区集镇地质灾害易发性建模的精度及合理性,基于ArcGIS数字高程模型栅格数据,选取地形地貌、岩性、距断层距离、归一化植被指数(normalized difference vegetation index,NDVI)等11个因子,采用信息量、信息量-逻辑回归耦合模型对麻城市滑坡、崩塌灾害进行地质灾害易发性建模,最终通过栅格最大值提取合成综合地质灾害易发性评价模型。通过对单一模型与耦合模型的结果进行ROC(receiver operating characteristic curve)检验和合理性验证,以期表明信息量-逻辑回归(I+LR)耦合模型较传统信息量(I)模型评价结果更加合理、精度更高,耦合模型能够较为准确地评价山区地质灾害易发性。

基于敏感承灾体的雷电灾害风险区划因子优化研究

雷电灾害风险评估和区划是当前全国第一次自然灾害风险普查工作的重要内容。针对试点区县普查中发现的《雷电灾害调查与风险评估技术规范》中指标选取、指标空间精细度等方面存在的值得商榷和改进之处,本文提出以下优化方案:(1)致灾危险性因子选取优化:致灾危险性因子仅使用闪电定位观测数据。(2)承灾体暴露度因子空间尺度精细化:采用1 km×1 km高精度人口和经济数据直接进行栅格计算,而不是采用行政区域均值。(3)承灾体脆弱性因子选取优化:引入了承灾体类型因子,反映出不同承灾体类型的雷电灾害脆弱性和易损性的显著不同;将场所的防雷装置设计审核和按期检测情况作为防雷减灾能力考量指标。同时,本文以常州市金坛区为例,使用该技术方案进行了雷电灾害风险区划并与规范进行对比。结果表明此方案更加科学、合理,反映了评估区域承灾体的雷电灾害风险特征,也更加符合本次普查和评估区划工作精准施策、针对性开展防雷减灾工作的初衷。

秦巴山区地质灾害危险性评价研究——以汉中市为例

我国秦巴地区地质灾害多发,为了探索该区域地质灾害危险性分布特征,以汉中市为研究区,根据区内生态地质环境特点,选取海拔、坡度、岩土类型、距断层距离、距河流距离、距道路距离、年降水量、归一化植被指数、土地利用类型等9项因子构建地质灾害危险性评价指标体系。利用层次分析法和主成分分析法求得各评价指标的组合权重,在ArcGIS空间分析软件中将评价因子信息量值乘以指标的组合权重,再进行叠加分级处理,得到了研究区地质灾害危险性分区评价结果。结果表明:(1)根据信息量值和叠加分析得出断裂构造是研究区地质灾害发生的主控因子,且在临近河流、道路的区域极易发生地质灾害。(2)汉中市地质灾害极高、高危险区占全区面积的39.34%,主要横贯研究区中部;中危险区在境内分布相对零散,呈“大分散小集中”的特点,其中在汉中盆地地带较为集中;低、极低危险区占总面积的35.26%,主要位于研究区的北部和南部。

GIS支持下基于CF和CF-LR模型的恩施州滑坡灾害易发性评价

湖北省恩施州内地质条件复杂,境内地质灾害数量众多,尤以滑坡为甚.以该州为研究范围,择取了包括地表坡度、斜坡坡型、坡向、构造、道路、地表水系、地层岩性、植被覆盖率8个方面的影响因素,基于ArcGIS平台统计分析空间数据的功能,分别采用确定系数模型及确定系数和逻辑回归耦合模型的方法进行区域滑坡地质灾害易发性评价,再通过对验证集灾害点在各个分区内的遍布情况和AUC值的比对进行两种模型的精度验证.结果表明两种模型易发性分区结果大体上一致,耦合模型的精度略高一筹.基于该组合模型计算出的易发值,将恩施州滑坡灾害易发性等级划为低易发区、中易发区、高易发区和极高易发区,为该地区地质灾害防治提供支撑.

废弃矿山环境地质影响及生态修复模式分析

矿产资源开采挖掘过程中,不可避免地会对矿山生态环境和地质地貌产生破坏和污染。随着绿色矿山建设及可持续发展理念的持续深入,对开采过的废弃矿山进行生态修复刻不容缓。为了给矿山修复工作提供一定的参考,对目前矿山修复的研究现状进行了整理。综述了废弃矿山对环境的多方面影响,包括地形地貌景观破坏、土地资源破坏、含水层破坏和引发地质灾害等;指出了在矿山开采前后,对矿山的环境影响及地质灾害风险、土壤及地下水污染风险进行评估是不可或缺的步骤;最后针对矿山对环境的多方面影响,从恢复矿山开采后的生态环境和恢复矿山稳定性两个方面指明了今后矿山修复的方向。

百色电网输电通道边坡地灾隐患点识别与风险管控技术

收集百色市历史地灾点作为地灾易发性评估的灾害基础数据集,优选地形地貌、地质条件、环境条件、人类活动等多项致灾评估因子构建符合百色市地域特征的地灾易发性评估模型。将卫星遥感中的InSAR(合成孔径雷达干涉)技术引入电网输电通道的广域微形变监测中,结合地灾易发性评估模型进行电网边坡的地灾隐患点识别。基于地质灾害风险管控的研究,提出针对电网输电通道边坡的相关地质灾害风险管控措施。该风险评估方法联合微形变动态指标和多项致灾评估因子等静态指标,通过指标优选分析,使模型更具科学性和实用性,能够实现对百色市输电通道边坡地灾隐患点的高精度、高效的风险排查以及提供合理的风险管控措施。

堤防工程险情风险评估与管控研究进展

堤防是湖泊和平原河道防止洪水灾害的重要设施,一旦出现险情进而诱发失事破坏,损失后果非常严重,世界各地均高度重视堤防工程险情风险评估与管控问题。然而,目前对堤防工程系统中堤身材料性质、地震和极端强降雨等不确定性因素量化表征不够,对堤防险情赋存环境认识不清,导致难以阐明堤防险情灾变机制与演变过程。此外,当前堤防险情风险评估方法仍以半定性半定量评价为主,缺少相应的堤防险情后果综合定量评价方法与衡量标准,致使堤防险情风险管控措施效果不佳。为此,分别从堤防险情赋存环境识别和表征、堤防险情灾变机制与演变过程、堤防险情后果分析、堤防险情风险评估及堤防险情风险管控5个方面进行文献调研和实地考察,概括总结这5个方面的国内外研究进展和目前存在的问题,进而初步归纳堤防工程险情风险评估与管控领域未来重点研究内容。研究成果有望为科学揭示堤防工程灾变机制,提出切合实际的堤防险情风险定量评估方法,构建基于风险的堤防工程信息化与标准化管控体系提供重要思路和理论支持。

铁路地质灾害早期识别与监测预警技术及应用研究

地质灾害隐患是我国山区铁路运输安全面临的巨大威胁,及早发现、诊断和处置灾害隐患对于铁路安全风险防范具有重要意义。为满足隐患精细化防治需求,系统归纳总结了我国山区运营铁路普遍存在的地质灾害隐患类型及特征,通过将灾害筛查、危险性评估与监测预警技术有机结合与融合应用,提出了基于天空地多源遥感和人工智能的铁路地质灾害早期识别与监测预警技术路线。研究表明:(1)我国山区铁路的地质灾害隐患主要表现出高位陡坡多、隐蔽性和潜伏性强、受工程扰动影响大、远程链式致灾和缓变型隐患分布广泛5种突出特征;(2)联合采用卫星遥感、InSAR、机载LIDAR和无人机遥感技术,实现了不同类型灾害隐患的精细化识别,形成了多层次立体化的铁路沿线隐患筛查技术方法;(3)融合应用机器学习、图像识别及变化检测等人工智能方法,提出了铁路沿线灾害危险性评估与监测预警技术路线,并通过工程应用验证了技术方案的有效性;(4)结合铁路工程特点,提出了以“区域-区段-工点”为主线的铁路地质灾害三级防控策略,构建了适用于铁路运维的地质灾害早期识别与监测预警技术体系。