A modified approach for semi-quantitative estimation of physical vulnerability of buildings exposed to different landslide intensity scenarios   总被引：1，自引：0，他引：1  

Aditi Singh  Shilpa Pal 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2019,13(1):66-81

Landslides are the most common natural disasters in mountainous regions, being responsible for significant loss of life as well as damage to critical infrastructure and properties. As the world population grows, people tend to move to higher locations to construct buildings, thereby making structures vulnerable due to landslides. This paper discusses previous research on the vulnerability assessment of structures exposed to landslides and presents a modified semi-quantitative approach to assess the scenario-based physical vulnerability of buildings based on their resistance ability and landslide intensity. Resistance ability is determined by integrating expert knowledge-based resistance factors assigned to five primary building parameters. Landslide intensity matrix defining different intensity levels is proposed based on combinations of landslide velocity and volume. Physical vulnerability of buildings is estimated and classified as class I, II or III for scenario-based low to very high landslide intensity. Finally, the application of the model is illustrated with a case study of 71 buildings from Garhwal Himalayas, India.  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   

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   

GPS RTK技术用于滑坡动态实时变形监测的研究   总被引：2，自引：0，他引：2  

王利  张勤  李寻昌  张永奇  管建安  涂锐 《工程地质学报》2011,19(2):193-198

为了研究GPS RTK技术用于滑坡动态变形监测的精度和可靠性,本文结合某类滑坡的大型物理模型试验,在滑坡体上布设了若干监测点,并用GPS RTK技术、全站仪三维测量技术和GPS单历元定位技术实时跟踪监测了该滑坡在自然状态下从稳定到产生破坏的全部过程。通过对监测数据的处理和分析,获得了RTK技术用于滑坡变形监测的可靠性和精度等技术参数,即在基准站和流动站同步观测到的卫星数在7颗以上且RTK系统的数据链能够正常工作的情况下,RTK测量的平面和高程精度就能分别控制在15mm和20mm以内。研究结果表明,RTK技术在一定条件下完全可用于滑坡灾害的动态实时变形监测。  相似文献   

含软弱夹层库岸滑坡滑带发育特征研究     

肖拥军  殷坤龙  柴波 《工程地质学报》2011,19(1):65-69

本文分析了巴东新城区巴东组第3段岩体中软弱夹层的分布特征、滑坡滑带的发育特征,结果表明巴东组第3段岩体中发育的滑坡滑带可与原岩中的软弱夹层对应,软弱夹层受构造剪切和地下水泥化作用发育成以碎石夹泥或黏土夹碎石为主的滑带.分析认为黄土坡滑坡、赵树岭滑坡的深层变形与巴东组第3段次级褶皱发育、层间劈理密集导致岩体破碎有关,而两...  相似文献   

彭州市干溪沟流域地震诱发地质灾害成因分析     

魏云杰  鲍艳  李战鲁 《工程地质学报》2011,19(4):582-587

干溪沟属于湔江的支流,在大地构造部位上位于龙门山断褶带中段前缘,地貌上属于侵蚀构造地貌和河流地貌,切割深,降雨量丰富,河谷、河流较发育.由于人工开采矿石普遍,地质灾害较发育,典型的地质灾害主要有大白岩崩塌体、大团包滑坡体、干溪沟潜在泥石流.大白岩逆冲崩塌体是在汶川大地震发生时,映秀一北川断层发生逆冲,上盘灰岩错出山坡,...  相似文献   

瓦屋山水电站厂房区红层岩体古滑坡稳定性研究     

王子忠  许模  杨绍平 《工程地质学报》2011,19(2):213-218

红层岩体以其岩体结构的软硬相间及软弱夹层发育而在边坡稳定方面表现为"易滑地层".瓦屋山水电站厂房区边坡为顺向坡,砂岩类岩石与黏土岩类岩石呈缓倾、互层状(倾角10°～20°)产出.构造与表生改造作用下,在这两类软硬相间的岩石界面上常形成分布较为连续的软弱夹层,这类岩体较易沿软弱夹层产生顺层滑动.地质历史时期,瓦屋山厂房区...  相似文献   

2010年4月14日玉树地震滑坡空间分布与控制变量分析   总被引：3，自引：0，他引：3  

许冲  徐锡伟  戴福初  王彦颖 《工程地质学报》2011,19(4):505-510

2010年4月14日07时49分,青藏高原中部,青海省玉树县发生了Ms7.1级大地震(玉树地震).震后遥感影像目视解译与实地调查结果表明,本次地震触发了2036处滑坡,总面积约1.194km2,这些滑坡大概分布在一个面积约1455.3km2的矩形区域内.本文分别使用滑坡数量与滑坡面积这2个标准,对玉树地震滑坡的地震控制...  相似文献   

"钻孔注浆+预应力锚索"对破碎地层滑坡的治理     

邹鲁德 《地质灾害与环境保护》2011,22(1):80-83,97

"钻孔注浆+预应力锚索"工艺被应用在了迪庆变电站滑坡山体的工程治理中.本文在分析滑坡体地层结构、岩性和地形特征的基础上,介绍工程设计、工艺和施工方案.工程效果表明,在破碎岩土区,"钻孔注浆+预应力锚索"工艺治理滑坡效果显著.  相似文献