GIS与土壤溶质运移模型结合研究进展

地理信息系统对土壤溶质模型研究而言，使用者的要求与GIS所能提供的功能之间，还存在巨大的差距。出于对环境问题的关注，土壤中水分及溶质运移的规律及其对环境的影响，成为当前研究的热点。随着对土壤溶质运移模型研究的深入，一方面由于田间土壤特性具有很大的空间变异；另一方面，在实际应用中，往往溶质运移的宏观特征而非微观特征，具有更重要的意义。将溶质运移模型与GIS技术结合，定量研究空间尺度的溶质运移，成为溶质运移研究的必然发展趋势。由于计算机软硬件技术的发展，GIS正处于一个高速发展的时期。将GIS与溶质运移模型相结合，或者进一步，以GIS理论和技术为基础，建立基于GIS的溶质运移模型，将对溶质运移的规律及其对环境的影响，做出更深入和准确的描述。     

我国低山丘陵区水土流失生态环境背景分析

我国是一个以山地为主的国家，山地多为大江大河的源头，而低山丘陵为这些大江大河支流的发源地，这些区域的水土流失必然会带来环境问题。本文以TM影象为数据源，以GIS为技术支撑，从DEM中获取低山丘陵层，以此为MASK层，提取水土流失及相应的生态环境背景各层，包括土地利用、坡度、年降水、积温、植被等生态背景层，这些背景层是多年数据的平均，然后把水土流失层与环境背景各层分别叠加，分析不同环境背景下水土流失状况，从而为治理水土流失提出科学依据。     

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。     

基于神经网络的单元自动机CA及真实和忧化的城市模拟

提出了一种基于神经网络的单元自动机（ＣＡ）。ＣＡ已被越来越多地应用在城市及其它地理现象的模拟中。ＣＡ模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂时，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考依据。     

A Reconnaissance Method for Delineation of Tracts for Regional-Scale Mineral-Resource Assessment Based on Geologic-Map Data

The U.S. Geological Survey (USGS) is proposing to conduct a global mineral-resource assessment using geologic maps, significant deposits, and exploration history as minimal data requirements. Using a geologic map and locations of significant pluton-related deposits, the pluton-related-deposit tract maps from the USGS national mineral-resource assessment have been reproduced with GIS-based analysis and modeling techniques. Agreement, kappa, and Jaccard's C correlation statistics between the expert USGS and calculated tract maps of 87%, 40%, and 28%, respectively, have been achieved using a combination of weights-of-evidence and weighted logistic regression methods. Between the experts' and calculated maps, the ranking of states measured by total permissive area correlates at 84%. The disagreement between the experts and calculated results can be explained primarily by tracts defined by geophysical evidence not considered in the calculations, generalization of tracts by the experts, differences in map scales, and the experts' inclusion of large tracts that are arguably not permissive. This analysis shows that tracts for regional mineral-resource assessment approximating those delineated by USGS experts can be calculated using weights of evidence and weighted logistic regression, a geologic map, and the location of significant deposits. Weights of evidence and weighted logistic regression applied to a global geologic map could provide quickly a useful reconnaissance definition of tracts for mineral assessment that is tied to the data and is reproducible.     

Identification of geomorphic process units in Kärkevagge, northern Sweden, by remote sensing and digital terrain analysis

Sediment transport processes in the Kärkevagge are investigated concerning their spatial and temporal characteristics due to long–term monitoring. Within this study remote sensing techniques and GIS modelling in connection with geomorphic mapping are applied for identification and characterization of geomorphic process units. Relationships between geomorphometric parameters and slope processes like solifluction, talus creep and rockfall have been analysed. Multitemporal Landsat–TM5 scenes are used as source for landcover characteristics (Normalized Difference Vegetation Index) after preprocessing involving orthorectification and topographic normalization in order to remove possible terrain–induced effects. Additionally, a digital elevation model with a resolution of 20 m for the Kärkevagge catchment is developed and parameters like slope gradient, slope aspect and profile curvature are extracted as input for the analysis of the sediment transport system. The combination of landcover information, geomorphometrical and topological features allows the definition of areas for single process activities. They show specific sediment displacement characteristics depending on material conditions, topological and geometrical features. Geomorphic process units, which show a homogenous composition, are extracted from these available layers.     

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.     

GIS专题数据综合的研究

随着GIS技术在各种应用领域的推广使用，各领域的专业数据与G1S的空间数据相结合，形成描述特定地理现象的各种专题数据。与GIS空间数据一样，专题数据的应用也由微观的、较为详细地描述小区域地理现象特征，转向宏观的、较为概略地描述较大范围地理现象特征，这是专题数据综合的主要任务。该文在讨论GIS专题数据类型及特征的基础上，着重分析了专题数据综合的基本类型，并提出了专题数据综合的概念模型，为GIS专题数据自动综合的实施提供理论依据和实施方案。     

空间数据库数据约束系统及其实现

空间数据库模型是构造地理信息系统的关键，如何在空间数据库中定义和实现空间实体间的约束关系直接影响到空间数据的完整性和一致性，是实现通用空间数据库约束系统的关键技术。该文讨论了传统GIS空间数据约束关系实现方法的局限性，提出了一个可扩展的空间数据约束描述和数据更新规则系统，并实现了系统原型。这一可扩展的约束系统将有利于解决GIS的多用户同步操作机制及分布式事务处理的实现问题。