Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks     

Husam A.H.Al-Najjar  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(2):625-637

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.  相似文献   

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management   总被引：1，自引：0，他引：1  

Zizheng Guo  Yu Shi  Faming Huang  Xuanmei Fan  Jinsong Huang 《地学前缘(英文版)》2021,12(6):243-261

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 zonation.This 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 subse-quently 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 model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter 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 fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,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 area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.  相似文献   

冲-洪积平原1:5万区域地质调查技术方法探索     

赵勇 《中国地质调查》2021,8(1):60-70

首都地区规划建设工作主要分布于平原松散地层之上,第四纪地质研究显得尤为重要。依托近几年来在北京地区开展的平原覆盖区1:5万区域地质调查和活动断裂专项调查项目成果,系统总结了平原区第四纪区域地质调查与评价的主要工作手段和技术方法,及其在解决第四纪基础地质问题及生态环境中所起的作用。通过平原区区域地质调查工作,可查明第四纪精细地质结构、含水层分布特征,精确厘定活动断裂位置及其活动时限,探讨自然环境演化序列与人类活动关系等,为城市规划、重大工程建设和应急水源地水资源合理开采提供基础地学数据。研究成果对首都城市减灾防灾、生态环境演变研究具有重要地学支撑作用。  相似文献   

泰青威天然气管道临朐段水毁灾害分布特征与风险评价     

张成信  魏龙飞  唐尧  王艳超  代晓光  任永健 《中国地质调查》2021,8(5):108-114

对泰青威天然气管道临朐段地质灾害发育特征进行调查研究发现,临朐段地质灾害类型主要为水毁灾害,具体可分为坡面水毁、河沟道水毁和台田地水毁。野外地质灾害实地调查临朐段管道沿线共发现地质灾害15处,其中坡面水毁点6处,河沟道水毁点4处,台田地水毁点5处。采用定性与半定量相结合的评价方法对其进行地质灾害风险评价,结果表明: 地质灾害风险等级较高的有1处,占6.66%; 风险等级中等的有4处,占26.67%; 风险等级较低的有10处,占66.67%。根据管道沿线地形地貌、地质灾害发育密度、风险等级等因素,划分地质灾害中易发区61 km,低易发区10 km,管道沿线以地质灾害中易发区为主。最后,针对不同类型、不同风险等级地质灾害提出了相应的防治消减措施,为后期管道安全运营和风险整治决策提供了有效的技术依据。  相似文献   

Parametric study on performance of bridge retrofitted by unseating prevention devices     

刘光晏  张国镇 《地震工程与工程振动(英文版)》2006,5(1):111-118

1 Introduction Structural retro?t techniques (Roberts, 2005), such as restrainers, concrete shear keys, steel jackets, CFRPshells, base isolators, or dampers have been widely studied and implemented in actual structures based on the experiences learned from past earthquakes. In the Chi-Chi earthquake (EERI, 2001), the damage to simply-supported PCI girder bridges provided a different view from which to examine the function of a rubber bearing system for simply- supported bridges (NCREE,…  相似文献   

加密自动气象站实时监控与查询显示系统   总被引：4，自引：2，他引：2  

杨晓武  黄兴友  徐平 《气象科技》2008,36(4):506-509

张家口于2005年建立首批60套雨量、温度两要素加密自动气象站,并于汛期投入应用.为有效利用加密 自动气象站资料,充分发挥加密自动气象站资料高时空分辨率的特点,结合日常业务运行流程和公众服务的需求, 研制了"加密自动气象站实时监控与查询显示系统".该系统集数据查询、统计计算、自动绘制雨量等值线图、文件 输出、报警监控等于一体,具有界面美观、易于操作、自动化程度高等特点.业务运行表明该系统功能实用、运行稳 定、查询速度快、输出图表清晰美观,适合在基层业务部门推广应用.  相似文献   

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer   总被引：1，自引：0，他引：1  

Wei Chen  Xi Chen  Jianbing Peng  Mahdi Panahi  Saro Lee 《地学前缘(英文版)》2021,12(1):93-107

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.  相似文献   

南疆暴雨研究进展          下载免费PDF全文

金晨  何清  黄乾 《大气科学学报》2023,46(1):82-96

南疆地区位于欧亚腹地,属于典型的温带大陆性干旱气候,受复杂地形地貌、天气系统路径以及特殊的大气环流与水汽条件等影响,暴雨突发性强且地域性特征显著。目前,全球数值预报模式及中尺度数值模式对南疆暴雨的预报能力十分有限,近年来,许多研究团队在塔里木盆地进行了大型外场观测试验,对揭示南疆暴雨的机制机理有了更多启示,对造成南疆暴雨的对流触发机制、高低空系统配置及演变特征、降雨云物理过程等都有了更为深入的认识。本文对南疆暴雨的气候特征、大尺度环流背景、中尺度系统发生发展、水汽输送、降水动力机制等方面进行了总结回顾,并提出了需要进一步研究的科学问题,以期为进一步开展南疆暴雨研究、提高暴雨预报准确率及防灾减灾能力提供参考。  相似文献   

灾区恢复重建后效评估研究进展          下载免费PDF全文

赵亮  何凡能  杨帆 《干旱区地理》2020,43(5):1337-1347

随着全球变化加剧,世界各地自然灾害的频发,国际社会为应对自然灾害进行了不懈努 力,历届世界减灾大会不断强调对应急管理全流程的研究,恢复重建作为应急管理的重要环节而 得到广泛重视。积极开展灾区恢复重建后效评估有利于保障灾区恢复重建实施与区域可持续发 展。灾区恢复重建后效评估研究时间较短,首先比较分析了国内外恢复重建的内涵,明确了恢复 重建后效评估的基本概念,并梳理了灾区恢复重建后效评估的在中国的发展演变。由于灾区恢复 重建内容复杂多样,本文结合灾区恢复重建后效评估的发展历程、研究范围与关注时段,分别从项 目、要素与可持续性三个关键视角对后效评估的理论方法等展开评述,结果表明：（1）项目后效评 估在灾区恢复重建后效评估中起步较早,现有评估多集中于居民住房、基础设施、公共设施等工程 质量的评估,但缺乏对项目设计过程中社会居民参与度、公众满意度以及社会经济效益等的评 估。（2）要素后效评估在灾区恢复重建后效评估中涉及范围最广,具体包括社会、经济与环境等要 素,这些要素的评估受政策绩效影响较大,后期需要构建综合的评估体系以开展科学评估。（3）可 持续性后效评估以联合国可持续发展目标与地方国民经济与社会发展计划为基础构建评估框架, 有利于促进灾区的可持续发展。通过综合分析《仙台减轻灾害风险框架》中“重建的更好”(BBB)理 念,联合国可持续发展目标（SDGs）以及《巴黎协定》适应全球变化等诉求,结合当前灾区恢复重建 后效评估现状进行展望,以期为灾区恢复重建与可持续发展提供一个更为系统、综合的技术参考。  相似文献   

移动GIS在传染病防控方面的应用——以COVID-19为例     

胡盼  何艺  刘韬  黄铁军  何超 《测绘通报》2020,(7):50-52

新型冠状病毒肺炎（COVID-19）疫情的出现和暴发流行,给社会、经济及人群健康提出巨大的挑战,已经成为重大公共卫生事件和社会问题。作为一种新发传染病,提早发现、迅速采用有效应对举措,是防止病毒蔓延扩散的重要环节。地理信息系统（GIS）在传染病的控制、预防、预警中有举足轻重的地位,移动GIS（Mobile GIS）作为GIS技术的发展,进一步提高了我国卫生部门应对突发传染病的能力。本文以COVID-19防控为例,重点介绍了移动GIS技术在传染病防控中的应用。  相似文献