Susceptibility regional zonation of earthquake-induced landslides in Campania,Southern Italy

In this paper, we present a GIS-based method for regional zoning of seismic-induced landslide susceptibility and show its application to the territory of the Campania region, Southern Italy. The method employs only three factors that we believe are most significant in the susceptibility assessment: the type of outcropping rock/soil, the slope angle, and the MCS intensity. Each of the three parameters is quantified in terms of relative weight expressed as indices, and the resulting Seismic Landslide Susceptibility index of an area is given by the average of the indices of the first two factors multiplied by the index of the third factor. The result of this susceptibility zonation applied to Campania shows a good agreement between the distribution of the historical earthquake-triggered landslides and the highly susceptible zones.     

Landslide susceptibility mapping using GIS-based multi-criteria decision analysis,support vector machines,and logistic regression

Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.     

河南省泥石流危险度分区

在分析历史资料和防治现状的基础上引入城市重要性作为判别准则之一,以18个地级市为研究对象利用层次分析法对河南省泥石流危险度分区。依据泥石流危险度权重的不同将河南省分为5个区间,其中危险度大的城市1个,危险度中等的城市3个,危险度小的城市6个,危险度极小城市2个,无危险度城市6个。以期为河南省泥石流灾害的防治提供有益的参考。     

滑坡灾害预测分区的计算机制图

本文在滑坡灾害预测分区的信息模型基础上,重点讨论了灾害预测的计算机制图化的主要过程:因素的数值化,单元边界的确定和彩色图件的绘制。运用中国地质大学计算机系开发的Mapcad系统,在Mv/10000计算机上较好地处理了不规则图幅边界的自然裁剪,不规则单元的输入,以及彩色图件的绘制等问题。     

Accounting for spatial patterns of multiple geological data sets in geological thematic mapping using GIS-based spatial analysis

This paper presents a Geographic Information System (GIS)-based spatial analysis scheme to account for spatial patterns and association in geological thematic mapping with multiple geological data sets. The multi-buffer zone analysis, the main part of the present study, was addressed to reveal the spatial pattern around geological source primitives and statistical analysis based on a contingency table was performed to extract information for the assessment of an integrated layer. Mineral potential mapping using multiple geological data sets from Ogdong in Korea was carried out to illustrate application of this methodology. The results obtained from the case study indicated that some geochemical elements and residual magnetic anomaly dominantly affected spatial patterns of the mineral potential map in the study area and the dominant classes of input data layers were also extracted. This information on spatial patterns of multiple geological data sets around mines could be used as effective evidences for the interpretation of the integrated layer within GIS.     

Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework

Natural Hazards - Pluvial flooding is a common type of natural hazard caused by rainfall events with high intensity and short duration, which may lead to substantial property damages,...     

Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation

This paper explains the procedure for the generation of a landslide risk index map at national level in Cuba, using a semi-quantitative model with ten indicator maps and a cell size of 90 × 90 m. The model was designed and implemented using spatial multi-criteria evaluation techniques in a GIS system. Each indicator was processed, analysed and standardised according to its contribution to hazard and vulnerability. The indicators were weighted using direct, pairwise comparison and rank-ordering weighting methods, and weights were combined to obtain the final landslide risk index map. The results were analysed per physiographic region and administrative units at provincial and municipal levels. The Sierra Maestra mountain system was found to have the largest concentration of high landslide risk index values while the Nipe–Cristal–Baracoa system has the highest absolute values, although they are more dispersed. The results obtained allow designing an appropriated landslide risk mitigation plan at national level and to link the information to the national hurricane early warning system, allowing also warning and evacuation for landslide-prone areas.     

Flood risk perceptions and spatial multi-criteria analysis: an exploratory research for hazard mitigation

The conventional method of risk analysis (with risk as a product of probability and consequences) does not allow for a pluralistic approach that includes the various risk perceptions of stakeholders or lay people within a given social system. This article introduces a methodology that combines the virtues of three different methods: the quantifiable conventional approach to risk; the taxonomic analysis of perceived risk; and the analytical framework of a spatial multi-criteria analysis. This combination of methods is applied to the case study ‘Ebro Delta’ in Spain as part of the European sixth framework project ‘Floodsite’. First, a typology for flood hazards is developed based on individual and/or stakeholders’ judgements. Awareness, worry and preparedness are the three characteristics that typify a community to reflect various levels of ignorance, perceived security, perceived control or desired risk reduction. Applying ‘worry’ as the central characteristic, a trade-off is hypothesized between Worry and the benefits groups in society receive from a risky situation. Second, this trade-off is applied in Spatial Multi-Criteria Analysis (SMCA). MCA is the vehicle that often accompanies participatory processes, where governmental bodies have to decide on issues in which local stakeholders have a say. By using risk perception-scores as weights in a standard MCA procedure a new decision framework for risk assessment is developed. Finally, the case of sea-level rise in the Ebro Delta in Spain serves as an illustration of the applied methodology. Risk perception information has been collected with help of an on-site survey. Risk perception enters the multi-criteria analysis as complementary weights for the criteria risk and benefit. The results of the survey are applied to a set of scenarios representing both sea-level rise and land subsidence for a time span of 50 years. Land use alternatives have been presented to stakeholders in order to provide the regional decision maker with societal preferences for handling risk. Even with limited resources a characteristic ‘risk profile’ could be drawn that enables the decision maker to develop a suitable land use policy.     

GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran,Iran)

The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region,northwest of Iran

Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.     

基于贡献率权重模型的川藏铁路沿线大型滑坡危险性区划

川藏交通廊道雅安到林芝段位于青藏高原东南部,沿线地质条件复杂、河流切割强烈、地质环境脆弱、新构造运动活跃,具有山高谷深、坡体稳定性差等特点,是我国崩滑灾害最发育、危害最严重的地区之一。为了保障廊道内相关工程的顺利建设和后期安全运营,本文以线路两侧一级分水岭为界,通过遥感解译和野外调查,获得川藏交通廊道雅安—林芝段崩滑灾害共4509处,在此基础上,选取高程、坡度、坡向、工程地质岩组、断裂、水系、公路、地震动峰值加速度、降雨共9个因子分析了灾害的空间分布规律及发育特征,建立了频率比法与逻辑回归方法耦合模型,并运用到高原山区重大交通廊道崩滑灾害危险性评价中。研究结果表明：(1)廊道沿线各县区段的崩滑灾害面密度在空间上总体呈从西向东递减的趋势。(2)有利于灾害发生的条件分别是：高程1~4 km,坡度大于20°,S、SW和W坡向,较软弱、较坚硬和坚硬岩组,距断裂6.4 km范围内,距水系3.2 km范围内,距公路800 m范围内,地震动峰值加速度0.20g,年均降雨量大于1100 mm。(3)将研究区危险性等级划分为极低危险(18.64%)、低危险(26.18%)、中等危险(24.75%)、高危险(19.82%)、极高危险(10.61%)5级,其中：极高危险区与高危险区主要分布在断裂附近和坡度较陡的区域。(4)耦合模型的AUC值达到了0.737,优于单一的频率比模型的0.712,表明耦合模型的评价结果具有更高的精度。该研究可为川藏交通廊道雅安到林芝段相关工程的规划、建设和未来运营过程中的防灾减灾工作提供重要参考。     

基于贡献率权重模型的川藏铁路沿线大型滑坡危险性区划

川藏铁路工程是国家重大基础设施建设项目,保障铁路的顺利建设和后期安全运营十分重要。铁路沿线发育广泛、危害严重的大型滑坡已成为全线的关键控制性问题,关乎工程建设的成败。以川藏铁路工程沿线大型滑坡作为主要研究对象,采用历史数据分析、实地调查、遥感解译的研究方法,基于ArcGIS平台,采用贡献率权重模型对铁路沿线区域进行了大型滑坡危险性评价,并利用自然断点法对危险性评价结果进行分区及统计分析。研究结果表明:川藏铁路沿线共发育大型、特大型滑坡共147处,其中大型滑坡106处,特大型滑坡41处,主要分布于白玉至江达段、昌都至八宿段、朗县至加查段等区段;铁路沿线处于高中低度三个等级危险区的面积分别为35918.5 km2、95484.3 km2和12039.7 km2,高度危险区大型滑坡分布密度为0.00199处/km?2,约为中度或低度危险区的2倍,高度危险区主要集中在邦达—八宿段、古乡—拉月段、白玉—江达段。根据贡献率权重模型求得的川藏铁路沿线大型滑坡危险度等级与野外实地调查的大型滑坡分布密度是一致的。相关研究成果可以为川藏铁路工程建设提供科学参考与依据。     

A multi-criteria method for assessment of spatial data infrastructure effectiveness

There is a demand for evaluation of spatial data infrastructures (SDIs), to justify and monitor the relations between investments in SDI initiatives and the results obtained. It is also essential to pay particular attention to identifying user communities, and eliciting their assessment of effects deriving from an SDI. The paper introduces a concept of a multi-criteria method which allows to assess effectiveness of the SDI from the user perspective. The application of the proposed method in the Polish Spatial Data Infrastructure (PSDI) and its main access point to spatial data and services called ‘Geoportal 2’ as well as two groups of the national geoportal”s users (spatial planners and land surveyors) presents its potential. The total scores for the Geoportal 2 indicated the investment has potential and is quite effective, although some components of the PSDI (e.g. main access point, datasets, network services, software, hardware, procedures) may need improvements and additional analyses in the future. The contribution of this paper is the multi-criteria method which enables the analysis of outcomes, benefits (impacts) and business value of using SDI business project’s artifacts (outputs) considering the following dimensions: information and support provided, use process, user organizational performance, strategic alignment and business impact on user enterprise.     

基于ANN和GIS耦合模型的场地地震液化势评价系统研究

采用组件式GIS (COMGIS)技术开发了结合BP神经网络分析模型的场地地震液化势评价系统,调用水平成层土地震反应分析程序SHAKE91实现设定地震下地震动影响场的模拟。在VB下调用Matlab神经网络工具箱来完成场地地震液化势评价模型在COMGIS系统中的模块化;利用GIS技术对评价结果,即液化势等级进行空间复合,给出场地潜在的地层液化势空间分布图。研究表明,SHAKE91应用程序在系统菜单下可直接调用,实现地震动影响场计算的模块化;BP神经网络技术应用于场地地震液化势评价中能达到较为理想的效果;系统的GIS空间分析功能可使评价结果与场地信息进行空间匹配,实现目标场地潜在地震液化势的快速评估。     

土工结构地震滑动位移统计分析

土工结构在地震荷载下的滑动位移是评估结构安全性能的重要参数。采用一种新型的地震波选择方法,在强震数据库中选择修改地震波,以有效地在结构动力分析中引入不同特征地震波的影响。通过一个简单的土工结构地震滑移模型,系统地分析了结构基本周期和滑动面屈服系数对地震滑移概率及相应滑移距离的影响,并提出了滑动体在不同地震场景和基本周期条件下的滑移概率和累积滑动位移的统计模型,对基于性能的土工结构抗震设计具有重要的参考意义。     

GIS-based statistical analysis of the spatial distribution of earthquake-induced landslides in the island of Lefkada, Ionian Islands, Greece

This is the first landslide inventory map in the island of Lefkada integrating satellite imagery and reports from field surveys. In particular, satellite imagery acquired before and after the 2003 earthquake were collected and interpreted with the results of the field survey that took place 1 week after this strong (Mw?=?6.3) event. The developed inventory map indicates that the density of landslides decreases from west to east. Furthermore, the spatial distribution of landslides was statistically analyzed in relation to the geology and topography for investigating their influence to landsliding. This was accomplished by overlaying these causal factors as thematic layers with landslide distribution data. Afterwards, weight values of each factor were calculated using the landslide index method and a landslide susceptibility map was developed. The susceptibility map indicates that the highest susceptibility class accounts for 38 % of the total landslide activity, while the three highest classes that cover the 10 % of the surface area, accounting for almost the 85 % of the active landslides. Our model was validated by applying the approaches of success and prediction rate to the dataset of landslides that was previously divided into two groups based on temporal criteria, estimation and validation group. The outcome of the validation dataset was that the highest susceptibility class concentrates 18 % of the total landslide activity. However, taking into account the frequency of landslides within the three highest susceptibility classes, more than 85 %, the model is characterized as reliable for a regional assessment of earthquake-induced landslides hazard.