基于GIS的滑坡CF多元回归模型及其应用

目前基于GIS滑坡变形失稳危险性评价方法均有各自的局限性。本文在基于数据的数学统计模型的基础上提出CF多元回归模型。通过将滑坡确定性稳定系数与回归模型的融合，在一定程度以解决了滑坡评价过程中影响因子的选择和量化的问题，有利于建立准确的滑坡分析模型。最后，将模型应用于云南小江流域，进行了该区的滑坡空间分布及稳定性分析。     

On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong

This paper presents a statistical approach to study the spatial relationship between landslides and their causative factors at the regional level. The approach is based on digital databases, and incorporates such methods as statistics, spatial pattern analysis, and interactive mapping. Firstly, the authors propose an object-oriented conceptual model for describing a landslide event, and a combined database of landslides and environmental factors is constructed by integrating the various databases within such a conceptual framework. The statistical histogram, spatial overlay, and dynamic mapping methods are linked together to interactively evaluate the spatial pattern of the relationship between landslides and their causative factors. A case study of an extreme event in 1993 on Lantau Island indicates that rainfall intensity and the migration of the center of the rainstorm greatly influence the occurrence of landslides on Lantau Island. A regional difference in the relationship between landslides and topography is identified. Most of the landslides in the middle and western parts of the island occurred on slopes with slope angles of 25–35°, while in the eastern part, the corresponding range is 30–35°. Overlaying landslide data with land cover reveals that a large number of landslides occurred in the bareland and shrub-covered area, and in the transition zones between different vegetation types. The proposed approach can be used not only to analyze the general characteristics of such a relationship, but also to depict its spatial distribution and variation, thereby providing a sound basis for regional landslide prediction.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

基于网格的空间信息模型与服务技术研究

空间信息基础设施是实现数字城市的核心与基础，现有的万维网已经不能满足空间信息访问与处理的需要。该文对以空间信息为核心和基础的数据网格——空间信息网格的基础理论及网格中的空间信息模型与服务技术：进行研究。提出了空间信息网格的若干关键技术及网格中的空间信息模型的特点与要求，并对空间信息网格服务进行了初步的探讨。     

多维植被信息系统及其应用前景

3S(地理信息系统GIS、遥感RS、全球定位系统GPS)及其相关技术的发展，使得植被信息系统的构建和应用成为可能。早期的植被图没有涉及到高度维和时间维，其应用受到很大的限制。因此有必要建立一种基于时空的多维植被信息系统，它的组成包括地理信息系统GIS、数据处理模块DPM、数据存取模块DAM、植被分析模块VAM和交互显示模块IDM，可用于植被的时空分析、三维景观显示、植被—地形—气候关系研究以及植被综合管理等方面，特别适合于山地植被的研究。多维植被信息系统具备立体直观的用户友好界面和强大的时空数据处理能力，并可通过数据库和网络进行资源共享，使植被研究和管理自动化、无纸化，其应用前景广阔。     

云南省防洪空间信息系统构建

通过讨论应用地理信息系统（GIS）技术，立足现实对相关空间数据进行集成和整合处理，实现防洪空间信息的数字化、标准化，为优化防汛指挥工作手段提供基础。系统数据库饭知不同比例尺精度的空间基础数据以满足不同目的的需求，对相关水文、防洪设施、全省洪涝风险和重点区风险图、土地利用等图件进行系统整合。系统有较强的管理功能和分析功能，可提高云南省防洪空间信息的共享度和系统有效管理，为政府统一组织和指挥抗洪救灾提供重要的辅助决策支持手段。     

辽宁省抚顺市区地质灾害监测系统设计方案

抚顺市地质灾害监测系统，由5方面工作内容组成：(1)建立统一可靠的城市高程起算基准；(2)扩展现有的沉降监测网，使分散的监测点联合形成全面、统一的监测网；(3)对浑河断裂带主断层实施监测；(4)对西露天采坑北坡滑坡重点区和采煤沉陷重点区实施水平位移监测；(5)利用计算机技术和地理信息系统(GIS)技术，建立抚顺市地质灾害信患系统。该系统是城市地质灾害防治的基础工程。系统应用了先进的监测手段及数据处理技术。文章介绍了系统的设计方案。     

基于GIS的陕西省旬阳地区滑坡灾害危险性区划

西部地区是我国地质灾害的重灾区。随着西部大开发战略的实施，该地区即将开展大规模的基础建设、能源开发等。区域内的经济发展与地质灾害的矛盾将不可避免地暴露出来。为解决这一问题，论文选取中国滑坡重灾区的江汉流域开展灾害危险性区划应用研究。研究区选在旬阳地区的县城近郊，通过MAPGIS软件平台及其二次开发的滑坡灾害分析系统，采用规则网格单元划分方法，运用信息量模型对该区斜坡稳定性进行了．空间定量预测，并依信息量法的结果编制了该区的危险性预测分区图。为政府部门进行土地规划决策、避免在地质灾害易发区进行大规模土地开发和工程建设提供了科学依据。     

平原河网区域来水组成原理

对于一般树状分布河网，上一级河道总是汇入至下一级河道，位于河道下游断面的流量总是由其上游汇集而至。但对于平原河网地区，特别是人工控制建筑物众多，又受潮汐影响的地区(如太湖流域)，河网错综复杂，水流方向不定，要跟踪某个断面的水流去向，或某个河段的水体、断面流量是从哪里汇集而来的，非常困难。但这样的问题在生产实践中往往对其很感兴趣。例如从常熟枢纽引长江水流进入望虞河后，流向何处?河网各断面流量中或各河段水体中常熟枢纽引江水量占多少比重?对这些问题的研究可以估计常熟枢纽引江的效果和影响范围。因此，平原河网区域来水组成方法在生产实践中具有重要意义。重点介绍了平原河网区域来水组成原理及其在太湖流域的应用。