Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview

The aim of this paper is to discuss a number of issues related to the use of spatial information for landslide susceptibility, hazard, and vulnerability assessment. The paper centers around the types of spatial data needed for each of these components, and the methods for obtaining them. A number of concepts are illustrated using an extensive spatial data set for the city of Tegucigalpa in Honduras. The paper intends to supplement the information given in the “Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning” by the Joint ISSMGE, ISRM and IAEG Technical Committee on Landslides and Engineered Slopes (JTC-1). The last few decades have shown a very fast development in the application of digital tools such as Geographic Information Systems, Digital Image Processing, Digital Photogrammetry and Global Positioning Systems. Landslide inventory databases are becoming available to more countries and several are now also available through the internet. A comprehensive landslide inventory is a must in order to be able to quantify both landslide hazard and risk. With respect to the environmental factors used in landslide hazard assessment, there is a tendency to utilize those data layers that are easily obtainable from Digital Elevation Models and satellite imagery, whereas less emphasis is on those data layers that require detailed field investigations. A review is given of the trends in collecting spatial information on environmental factors with a focus on Digital Elevation Models, geology and soils, geomorphology, land use and elements at risk.     

Contribution of the SISCam Web-based GIS to the seismotectonic study of Campania (Southern Apennines): an example of application to the Sannio-area

In this article the implementation and potential of the Seismotectonic Information System of the Campania Region (SISCam) are described, in particular an application of this Web-based GIS system to the seismotectonic analysis of the Sannio area (Southern Apennines) is performed. WEB-GIS technologies greatly contribute to both the environmental monitoring and the disaster management of areas affected by high natural risks. Specifically the SISCam system has been developed with the aim of providing easy access and fast diffusion, through Internet technology, of the most significant geological, geophysical, and territorial data relative to the Campania Region. The Sannio area has been selected as our application example because it is among the most active seismic regions in Italy. This portion of the Southern Apennines which was hit by the June 5, 1688 strong earthquake (M _W = 6.7, CPTI 1999) and by some low- and moderate-energy seismic sequences (1990–1992, 1997), is characterized by a complex inherited tectonic setting and low-tectonic deformation rates that hide the seismogenic sources position. Since this case study turned out to be complicated, the use of the SIScam WEB-GIS has become indispensable because it allowed us to visualize, integrate and analyze all the data available, in order to obtain an accurate and direct picture of the seismotectonic setting of the area. Moreover, a different approach of data analysis was necessary, due to the lack of up-to-date neotectonic and structural data; therefore, the operation of this GIS system enabled us to process and generate some original informative layers, through image analysis, such as new structural lineaments represented on a map of the potential active faults of the area, which has been the final result of our application, as a contribution to new knowledge about the local seismic risk parameters.     

Analysis of the 2003 Varunawat Landslide,Uttarkashi, India using Earth Observation data

Mass movements such as landslides in mountainous terrains are natural degradation processes and one of the most important landscape-building factors. Varunawat Parbat overlooking Uttarkashi town witnessed a series of landslides on 23 September 2003 and the debris slides and rock falls continued for 2 weeks. This landslide complex was triggered due to the incessant rainfall prior to the event, and its occurrence led to the blockage of the pilgrim route to Gangotri (source of the Ganges river) and evacuation of thousands of people to safer places. Though there was no loss of lives due to timely evacuation, heavy losses to the property were reported. High-resolution stereoscopic earth observation data were acquired after the incidence to study the landslide in detail with emphasis on the cause of the landslide and mode of failure. Areas along the road and below the Varunawat foothill region are mapped for landslide risk. It was found that the foothill region of the Varunawat Parbat was highly disturbed by man-made activities and houses are dangerously located below steep slopes. The potential zones for landslides along with the existing active and old landslides are mapped. These areas are critical and their treatment with priority is required in order to minimise further landslide occurrences.     

三峡库区生基包滑坡监测及成果分析

生基包滑坡监测属于三峡库区奉节县三期地质灾害监测预警项目之一,该滑坡位于长江左岸,临近人口稠密的安坪乡集镇,地理位置重要。三峡水库175 m蓄水后,其变形破坏特征有何表现？对航道安全运营有无潜在威胁？是否会对滑坡体上的重要建筑及村民生产生活构成危害？针对这些问题,首先分析了滑坡的工程地质特征及主要的影响因素;其次,确立以4种监测手段为主、人工巡查为辅的监测方案;通过对大地变形GPS、深部位移、滑坡推力等几种监测方法的运用及对其成果进行分析研究,以实例说明其在滑坡监测中的应用;再次,结合宏观人工巡查进行对照分析,以充分说明大地变形、深部位移和滑坡推力监测在实际运用中的可行性;最后,根据监测结论提出对生基包滑坡防治的建议。     

GPS变形监测平差计算及不确定度分析

全球定位系统（GPS）是一种全天候、高精度的连续定位系统,它以速度快、方法灵活多样、操作简便等优势被广泛应用于工程测量和变形监测中。结合水厂铁矿GPS边坡变形监测实例,对GPS监测网的星历预报、基线向量平差计算、网平差计算、结果及残差不确定度进行了细致分析研究,以验证GPS技术在边坡变形监测中的可靠性和精度。     

基于AGA的SVM需水预测模型研究

需水预测是一个由城市人口、工业水平、社会经济水平共同作用的多因素、多层次的复杂非线性系统.其结果将直接影响受区域水资源承载力约束的产业结构、布局形态等决策.作为一种集中参数预报方法,支持向量机方法具有对未来样本的较好的泛化性能,对于这类资料缺乏、系统结构尚欠清晰的问题可以取得较好的模拟和预测结果.基于此,本文将支持向量机方法引入需水预测领域,建立了需水预测支持向量机模型.同时,本文将加速遗传算法和支持向量机方法耦合起来,构造了支持向量机模型参数的自适应优化算法.模型在珠海市的应用实例表明:与简单遗传算法比较,AGA的模型参数寻优效率更高;与BP神经网络模型相比,SVM模型较好地解决了小样本、经验性等问题,并取得了较高的预测精度.     

Predictability of Interannual Variability in the Kuroshio Transport South of Japan Based on Wind Stress Data over the North Pacific

It is expected that a roughly two-year forecast of the Kuroshio transport variation can be made from a past record of wind stress data over the ocean, since it takes nearly ten years for the first-mode baroclinic Rossby wave to traverse the entire basin in the midlatitude North Pacific (∼30°N). We therefore investigated the predictability using an ocean general circulation model driven by the wind stress data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. Referring to a hindcast experiment as the control run, we carried out fifteen forecast experiments, the initial conditions of which are taken from the hindcast experiment at intervals of two years during the period from the end of 1969 to the end of 1997. Each of the forecast experiments is driven only by wind stress in the year preceding each experiment. The forecasted Kuroshio transport anomaly south of Japan agrees better with the hindcasted one during the first two years of the forecast in most cases. In some cases, however, significant disagreements occur, most of which are likely due to larger unpredictable variations caused by wind stress anomalies near Japan. At the end of forecast year 2, the anomaly correlation coefficient is about 0.7, and rms of the forecast error is smaller than rms of the hindcasted anomaly. These results indicate that the prediction of the interannual variability in the Kuroshio transport could be made two years in advance at a statistically significant level. This revised version was published online in July 2006 with corrections to the Cover Date.     

Prediction of local scour around circular piles under waves using a novel artificial intelligence approach

Abstract

The scour phenomena around vertical piles in oceans and under waves may influence the structure stability. Therefore, accurately predicting the scour depth is an important task in the design of piles. Empirical approaches often do not provide the required accuracy compared with data mining methods for modeling such complex processes. The main objective of this study is to develop three data-driven methods, locally weighted linear regression (LWLR), support vector machine (SVR), and multivariate linear regression (MLR) to predict the scour depth around vertical piles due to waves in a sand bed. It is the first effort to develop the LWLR to predict scour depth around vertical piles. The models simulate the scour depth mainly based on Shields parameter, pile Reynolds number, grain Reynolds number, Keulegan–Carpenter number, and sediment number. 111 laboratory datasets, derived from several experimental studies, were used for the modeling. The results indicated that the LWLR provided highly accurate predictions of the scour depths around piles (R?=?0.939 and RMSE = 0.075). Overall, this study demonstrated that the LWLR can be used as a valuable tool to predict the wave-induced scour around piles.