山区公路滑坡灾害问题及对策分析--以云南元(江)磨(黑)高速公路为例   总被引：8，自引：4，他引：4  

苏生瑞  孙芳强  吴华金  刘汝民  刘红卫 《工程地质学报》2005,13(3):340-345

随着西部公路建设的蓬勃发展,越来越多的高等级公路必须穿越山区丘陵地带,这样必然导致大量的公路滑坡的出现,危及公路的正常运行及安全。本文以云南元(江)磨(黑)高速公路为例,在分析公路沿线自然环境条件及地质构造条件等的基础上,研究公路沿线的地质灾害的特点及危害,重点研究公路沿线所发育的滑坡的分布特征、危害、形成机制及整治对策和措施。研究表明,山区公路滑坡不仅与滑坡发育的工程地质条件有关,而且与工程设计方案及公路建设过程施工方案等人为因素有关。  相似文献   

Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy   总被引：6，自引：3，他引：3  

F. Falaschi  F. Giacomelli  P. R. Federici  A. Puccinelli  G. D’Amato Avanzi  A. Pochini  A. Ribolini 《Natural Hazards》2009,50(3):551-569

This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models (conditional analysis and logistic regression), and neural network application, in order to better understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological, and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means of indirect-quantitative statistical methods and neural network software application. Experimental results from different methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic (ROC) curves analysis approach.  相似文献   

Landslide Research at the British Geological Survey:Capture,Storage and Interpretation on a National and Site-Specific Scale     

Catherine PENNINGTON  Claire FOSTER  Jonathan CHAMBERS  Gareth JENKINS 《《地质学报》英文版》2009,83(5):991-999

Abstract: Landslide research at the British Geological Survey (BGS) is carried out through a number of activities, including surveying, database development and real-time monitoring of landslides. Landslide mapping across the UK has been carried out since BGS started geological mapping in 1835. Today, BGS geologists use a combination of remote sensing and ground-based investigations to survey landslides. The development of waterproof tablet computers (BGS·SIGMAmobile), with inbuilt GPS and GIS for field data capture provides an accurate and rapid mapping methodology for field surveys. Regional and national mapping of landslides is carried out in conjunction with site-specific monitoring, using terrestrial LiDAR and differential GPS technologies, which BGS has successfully developed for this application. In addition to surface monitoring, BGS is currently developing geophysical ground-imaging systems for landslide monitoring, which provide real-time information on subsurface changes prior to failure events. BGS’s mapping and monitoring activities directly feed into the BGS National Landslide Database, the most extensive source of information on landslides in Great Britain. It currently holds over 14?000 records of landslide events. By combining BGS’s corporate datasets with expert knowledge, BGS has developed a landslide hazard assessment tool, GeoSure, which provides information on the relative landslide hazard susceptibility at national scale.  相似文献   

Landslide and basin self-organized criticality in the Lushan Hot Spring area     

Chien-Yuan CHEN 《山地科学学报》2012,9(4):463-471

Defining a basin under a critical state(or a self-organized criticality) that has the potential to initiate landslides,debris flows,and subsequent sediment disasters,is a key issue for disaster prevention.The Lushan Hot Spring area in Nantou County,Taiwan,suffered serious sediment disasters after typhoons Sinlaku and Jangmi in 2008,and following Typhoon Morakot in 2009.The basin’s internal slope instability after the typhoons brought rain was examined using the landslide frequency-area distribution.The critical state indices attributed to landslide frequency-area distribution are discussed and the marginally unstable characteristics of the study area indicated.The landslides were interpreted from Spot 5 images before and after disastrous events.The results of the analysis show that the power-law landslide frequency-area curves in the basin for different rainfall-induced events tend to coincide with a single line.The temporal trend of the rainfallinduced landslide frequency-area distribution shows 1/f noise and scale invariance.A trend exists for landslide frequency-area distribution in log-log space for larger landslides controlled by the historical maximum accumulated rainfall brought by typhoons.The unstable state of the basin,including landslides,breached dams,and debris flows,are parts of the basin’s self-organizing processes.The critical state of landslide frequency-area distribution could be estimated by a critical exponent of 1.0.The distribution could be used for future estimation of the potential landslide magnitude for disaster mitigation and to identify the current state of a basin for management.  相似文献   

Contrasting rainfall generated debris flows from adjacent watersheds at Forest Falls, southern California, USA   总被引：1，自引：0，他引：1  

Douglas M. Morton  Rachel M. Alvarez  Kelly R. Ruppert  Brett Goforth 《Geomorphology》2008,96(3-4):322

Debris flows are widespread and common in many steeply sloping areas of southern California. The San Bernardino Mountains community of Forest Falls is probably subject to the most frequently documented debris flows in southern California. Debris flows at Forest Falls are generated during short-duration high-intensity rains that mobilize surface material. Except for debris flows on two consecutive days in November 1965, all the documented historic debris flows have occurred during high-intensity summer rainfall, locally referred to as ‘monsoon’ or ‘cloudburst’ rains. Velocities of the moving debris range from about 5 km/h to about 90 km/h. Velocity of a moving flow appears to be essentially a function of the water content of the flow. Low velocity debris flows are characterized by steep snouts that, when stopped, have only small amounts of water draining from the flow. In marked contrast are high-velocity debris flows whose deposits more resemble fluvial deposits. In the Forest Falls area two adjacent drainage basins, Snow Creek and Rattlesnake Creek, have considerably different histories of debris flows. Snow Creek basin, with an area about three times as large as Rattlesnake Creek basin, has a well developed debris flow channel with broad levees. Most of the debris flows in Snow Creek have greater water content and attain higher velocities than those of Rattlesnake Creek. Most debris flows are in relative equilibrium with the geometry of the channel morphology. Exceptionally high-velocity flows, however, overshoot the channel walls at particularly tight channel curves. After overshooting the channel, the flows degrade the adjacent levee surface and remove trees and structures in the immediate path, before spreading out with decreasing velocity. As the velocity decreases the clasts in the debris flows pulverize the up-slope side of the trees and often imbed clasts in them. Debris flows in Rattlesnake Creek are relatively slow moving and commonly stop in the channel. After the channel is blocked, subsequent debris flows cut a new channel upstream from the blockage that results in the deposition of new debris-flow deposits on the lower part of the fan. Shifting the location of debris flows on the Rattlesnake Creek fan tends to prevent trees from becoming mature. Dense growths of conifer seedlings sprout in the spring on the late summer debris flow deposits. This repeated process results in stands of even-aged trees whose age records the age of the debris flows.  相似文献   

A Neogene giant landslide in Tarapacá, northern Chile: A signal of instability of the westernmost Altiplano and palaeoseismicity effects     

L. Pinto  G. Hrail  S.A. Sepúlveda  P. Krop 《Geomorphology》2008,102(3-4):532-541

Giant landslides, which usually have volumes up to several tens of km³, tend to be related to mountainous reliefs such as fault scarps or thrust fronts. The western flank of the Precordillera in southern Peru and northern Chile is characterized by the presence of such mega-landslides. A good example is the Latagualla Landslide (19°15′S), composed of ~ 5.4 km³ of Miocene ignimbritic rock blocks located next to the Moquella Flexure, a structure resulting from the propagation of a west-vergent thrust blind fault that borders the Precordillera of the Central Depression. The landslide mass is very well preserved, allowing reconstitution of its movement and evolution in three main stages. The geomorphology of the landslide indicates that it preceded the incision of the present-day valleys during the late Miocene. Given the local geomorphological conditions 8–9 Ma ago (morphology, slopes and probably a high water table), large-magnitude earthquakes could have provided destabilization forces enough to cause the landslide. On the other hand, present seismic forces would not be sufficient to trigger such landslides; therefore the hazard related to them in the region is low.  相似文献   

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression   总被引：13，自引：0，他引：13  

M.J. García-Rodríguez  J.A. Malpica  B. Benito  M. Díaz 《Geomorphology》2008,95(3-4):172-191

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.  相似文献   

Hawaii bibliographic database     

Thomas L. Wright  Taeko Jane Takahashi 《Bulletin of Volcanology》1998,59(4):276-280

 The Hawaii bibliographic database has been created to contain all of the literature, from 1779 to the present, pertinent to the volcanological history of the Hawaiian-Emperor volcanic chain. References are entered in a PC- and Macintosh-compatible EndNote Plus bibliographic database with keywords and abstracts or (if no abstract) with annotations as to content. Keywords emphasize location, discipline, process, identification of new chemical data or age determinations, and type of publication. The database is updated approximately three times a year and is available to upload from an ftp site. The bibliography contained 8460 references at the time this paper was submitted for publication. Use of the database greatly enhances the power and completeness of library searches for anyone interested in Hawaiian volcanism. Received: 1 June 1997 / Accepted: 17 September 1997  相似文献   

Landslide intelligent prediction using object-oriented method     

Xianmin Wang  Ruiqing Niu 《Soil Dynamics and Earthquake Engineering》2010

Landslides are one of the most serious geological disasters in the world and happen quite frequently in the Three Gorges. Landslide prediction is a very important measure of landslide prevention and cure in the Three Gorges. Traditional methods lack in sufficiently mining the various complex information from a landslide system. They often need much manual intervention and possess poor intelligence and accuracy. An intelligent method proposed in this paper for landslide prediction based on an object-oriented method and knowledge driving is hopeful to solve the above problem. The method adopted Landsat ETM+ images, 1:50,000 geological map and 1:10,000 relief map in the Three Gorges as the data origins. It firstly produced the key factors influencing landslide development and used multi-resolution segmentation algorithm to segment the image objects based on the key landslide factors of engineering rock group, reservoir water fluctuation, slope structure and slope level. Secondly, the method chose some sample objects and adopted the decision tree algorithm C5.0 to mine the landslide forecast criteria according to the factor values of each sample object. Finally, under knowledge driving the method classified the image objects and realized landslide susceptibility analysis and intelligent prediction in the Three Gorges. The method proposed in this paper is object-oriented. Results of a real-world example show that: (1) the object-oriented method possesses much more compact knowledge representation, higher efficiency, more continuous classifying result and higher prediction accuracy compared with the pixel-oriented method; (2) it possesses the overall accuracy of 87.64% and kappa coefficient of 0.8305 and is more accurate than the other seven methods (such as the pixel-oriented methods of Parallelpiped, Minimum Distance, Maximum Likelihood, Mahalanobis Distance, K-means and Isodata and the object-oriented method of Nearest Neighbor); (3) about 46.97% landslides lie in the high susceptibility region, 24.24% landslides lie in the moderate susceptibility region, 27.27% landslides lie in the low susceptibility region and 1.52% landslides lie in the very low susceptibility region. Therefore the method can effectively realize landslide susceptibility analysis and provides a new idea for landslide intelligent and accurate prediction.  相似文献   

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method   总被引：12，自引：4，他引：8  

John Mathew  V. K. Jha  G. S. Rawat 《Landslides》2009,6(1):17-26

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.  相似文献