降雨型浅层滑坡危险性预测模型

通过分析SHALSTAB和TRIGRS等浅层滑坡物理确定性模型存在的问题,提出了基于降雨入渗动态守恒的瞬态降雨入渗模型,该模型考虑了初期降雨过程、降雨历程以及饱和非饱和入渗过程,证明了SHALSTAB模型是该模型的特殊形式,并克服了TRIGRS模型参数繁多及一维入渗路径的问题.将无限边坡模型、瞬态降雨入渗模型和GIS进行耦合,研发了可用于大范围降雨型浅层滑坡危险性预测的集成系统,根据边坡的地质条件、地形参数和降雨特征即可对降雨条件下浅层滑坡的危险性进行评估.     

哈尔滨市地下水开采安全警戒量的研究

明确了地下水开采安全警戒量的概念，建立了哈尔滨市地下水开采安全警戒量评价指标体系。以地下水过量开采所带来的环境负效应作为评价指标，对这些环境指标进行了量化分级、加权。运用GIS空间叠加手段，建立了研究区每个栅格的地下水开采安全警戒量评价体系，求出了哈尔滨市地下水开采安全警戒量。     

Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework,Chania, Northwestern Crete,Greece

Soil erosion is a growing problem in southern Greece and particularly in the island of Crete, the biggest Greek island with great agricultural activity. Soil erosion not only decreases agricultural productivity, but also reduces the water availability. In the current study, an effort to predict potential annual soil loss has been conducted. For the prediction, the Revised Universal Soil Loss Equation (RUSLE) has been adopted in a Geographical Information System framework. The RUSLE factors were calculated (in the form of raster layers) for the nine major watersheds which cover the northern part of the Chania Prefecture. The R-factor was calculated from monthly and annual precipitation data. The K-factor was estimated using soil maps available from the Soil Geographical Data Base of Europe at a scale of 1:1,000,000. The LS-factor was calculated from a 30-m digital elevation model. The C-factor was calculated using Remote Sensing techniques. The P-factor in absence of data was set to 1. The results show that an extended part of the area is undergoing severe erosion. The mean annual soil loss is predicted up to ∼200 (t/ha year⁻¹) for some watersheds showing extended erosion and demanding the attention of local administrators.     

空间分析方法、应用模型与GIS的关系

首先阐述了空间分析和应用模型的基本概念，进而从地理信息处理系统的功能入手，分析了空间分析、应用模型和GIS的关系，最后指出加强应用模型与GIS的结合以及增强GIS的基本空间分析功能是增强GIS整体功能的二个最有效的方法．     

ORACLE空间数据库的对象-关系模式初探——兼议关系数据库和面向对象数据库在GIS中的应用

传统的关系数据库在GIS的应用中主要存在以下问题: 除数据类型简单, 不能很好地表达非结构化数据外, 也难以表示GIS中具有复杂结构的数据; 关系模型简单, 不能表达数据之间的层次, 继承、聚合、泛化或特化等在GIS广泛使用的关系; 与数据操纵语言如SQL和通用程序设计语言, 特别是目前流行的面向对象设计语言之间失配.采用面向对象技术可以较好地解决上述问题.ORACLE的空间数据库在传统的关系数据库上进行了面向对象的扩展, 即所谓对象-关系模式, 其核心在于增加了名为SDO-GEOMETRY的对象数据类型, 用于存储几何实体.ORACLE的空间数据库的应用示例表明: 以面向对象的形式表示空间数据比之于关系数据库中存放大量无意义的坐标有着很大的优势, 同时, 它在复合对象的表达方面, 在对于空间关系的处理方面都非常有效      

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

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.     

基于三维地质信息模型的边坡风险分析

风险分析是降低边坡灾害损失的有效途径。以地理信息系统（Arcview GIS）为二次开发平台,建立边坡风险评估系统。该系统通过建立边坡的三维地质信息模型,根据模型的物理力学参数等属性数据,运用Monte-Carlo法求出三维边坡的失效概率,在易损性分析的基础上,计算边坡失稳的经济风险和生命风险。实例分析表明,基于三维地质信息模型的边坡风险分析可为相关风险决策提供科学依据     

基于GIS的地震安全性评价数据管理的研究

目前的地震安全性评价数据管理方法过于陈旧,不利于数据的查询和再利用。通过对这一问题的深入分析,针对安评数据的多元性特点,结合GIS的优势,提出了利用GIS技术对地震安全性评价数据进行管理的方法,并设计出了数据库管理系统的基本框架。该系统的建立,将为地震安全性评价数据管理、查询和再利用提供便利。     

A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate

The purpose of this study is to evaluate and to compare the results of multivariate (logical regression) and bivariate (landslide susceptibility) methods in Geographical Information System (GIS) based landslide susceptibility assessment procedures. In order to achieve this goal the Asarsuyu catchment in NW Turkey was selected as a test zone because of its well-known landslide occurrences interfering with the E-5 highway mountain pass.Two methods were applied to the test zone and two separate susceptibility maps were produced. Following this a two-fold comparison scheme was implemented. Both methods were compared by the Seed Cell Area Indexes (SCAI) and by the spatial locations of the resultant susceptibility pixels.It was found that both of the methods converge in 80% of the area; however, the weighting algorithm in the bivariate technique (landslide susceptibility method) had some severe deficiencies, as the resultant hazard classes in overweighed areas did not converge with the factual landslide inventory map. The result of the multivariate technique (logical regression) was more sensitive to the different local features of the test zone and it resulted in more accurate and homogeneous susceptibility maps.