Uncertain spatiotemporal data modeling and algebraic operations based on XML

The problem of modeling and operating spatiotemporal data has received a great deal of interest, due to its various applications in the real world such as GIS and sensor database. A wide range of work covering spatial data, temporal data and spatiotemporal data assumes that the data is known, accurate and complete. But in reality, information is often imprecise and imperfect. In addition, traditional data models which are investigating in the context of traditional database suffer from some inadequacy of necessary semantics such as inability to handle imprecise and uncertain information. Consequently, the advent of XML, which has the advantages of simplicity, readability and extensibility, seems to provide an opportunity for modeling and operating uncertain spatiotemporal data. Hence, the new problem that emerges is how to model and operate uncertain spatiotemporal data in XML. Therefore, in this paper, we establish an uncertain spatiotemporal data model based on XML. Then, on the basis of the model we provide a set of algebraic operations for capturing and handling uncertain spatiotemporal data. By employing algebraic operations, we demonstrate how to translate queries expressed in XQuery to our algebra. A translation example shows that our algebraic operations are full of expressive power and illustrates that our algebra can be applied to general data. Apart from this, we also propose a set of equivalence rules to optimize the process of query and give an example to show how the optimization approach works.     

基于SOA的国家级油气资源数据库ETL系统设计与实现

针对我国油气资源数据相对多源、异构、分散的现状,根据OLAP的实际需求,采用MVC模式作为系统架构模式,实现了国家油气资源数据库数据提取、转换和加载(ETL)系统。阐述了系统所采用的基于面向对象体系结构(SOA)的5层结构模型,使用ODBC和XML技术实现数据库连接的动态映射,应用Oracle DB解决了多媒体数据处理等关键问题。     

中国岩石圈数据模型总体设计

基于GIS的中国岩石圈三维结构数据库包含了数据类型各异的12个专业子库内容。存储和管理这些复杂的数据需要一个合理有效的数据模型,以确保数据库中的所有数据对象得到精确和完整的表达。本文讨论岩石圈数据模型的总体设计问题,基本思路是采用当前成熟的面向对象的软件开发技术和面向对象的地理数据库模型。总库数据模型需要满足扩展性和扩充性的要求。开发中用UML协助分析子库数据,在面向对象的地理数据库中用一个要素数据集来容纳一个子库的空间数据,用数据库表、要素类、关系类、子类、域等概念描述岩石圈研究中的各种概念和信息,形成统一的、空间和属性存储管理一体化的岩石圈数据模型。     

基于特征的面向对象虚拟GIS数据模型设计与原型系统实现

针对现有数据模型的不足,结合GIS数据模型的最新进展,在CAD、虚拟现实和仿真等领域得到应用的相关数据模型的基础上,提出使用基于特征的建模技术和面向对象的方法构建一个全新的基于特征的面向对象虚拟GIS数据模型。采用Oracle ORDB管理模式实现其面向对象的虚拟GIS数据库,并依据该模型设计了Client/Server层次结构的分布式虚拟GIS原型系统。     

Use of standard vocabulary services in validation of water resources data described in XML

The machine readable encoding language XML is used in water informatics to describe resources and observational data, such as the Water Data Transfer Format (WDTF). WDTF is part of an Australian initiative, established by the Australian Bureau of Meteorology in collaboration with CSIRO, to collate water resources data from multiple data providers into a national water information system. A common way of validating XML data is by defining a schema using XML Schema Definition language (XSD) and performing validation using standard XSD tools. However, XSD validation lacks the ability to perform content validation to assert context, domain and organizational rules such as soft-typing, co-constraints, and code-list or vocabulary checking, which is required in the validation of WDTF data. In this paper, we describe a validation service for validating water resources data encoded in WDTF, which combines structural and content validation. We also describe the use of a vocabulary service with the WDTF validation service to perform code-list and vocabulary checking.     

GIS支持下的洪水淹没范围模拟

洪水淹没范围的确定是洪灾损失评估的核心环节。采用地理信息系统 (GIS)与水力演进模型,结合三维模拟技术和对象关系模型数据库,对浙江奉化江流域洪水淹没范围进行模拟。该方法能够准确地模拟洪水淹没范围,为快速评估洪灾损失和防洪决策服务提供了科学的依据。     

An efficient knowledge-based approach for random variation interpretation in NDVI time series

Nowadays, detecting and interpreting random variation extracted from satellite image time series is a far-reaching real-world issue. A more adequate approach should be designed to deal with this challenge. In this paper, we propose an efficient knowledge-based approach for vegetation monitoring using normalized derivation vegetation index time series. First, a decomposition process is designed to separate seasonal, trend and remainder components. Then, a genetic based schema is proposed to generate association rules. The extracted knowledge is intended to interpret the remainder component extracted during the previous phase by discovering the hidden link between random variation and climate observations. For validation purpose, a database covering the regions in Northwestern Tunisia is used for a period starting from 2000 to 2012. The data have been derived from Moderate resolution Imaging Spectroradiometer and fused with ground climate data (temperature and precipitation) in the form of fuzzy association rules. The obtained results show the efficiency of the proposed approach by reducing the non-stationarity effect.     

Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County,North Carolina

The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.     

An interval fuzzy model for magnetic monitoring: estimation of a pollution index

In this contribution, a methodology is reported in order to build an interval fuzzy model for the pollution index PLI (a composite index using relevant heavy metal concentration) with magnetic parameters as input variables. In general, modelling based on fuzzy set theory is designed to mimic how the human brain tends to classify imprecise information or data. The “interval fuzzy model” reported here, based on fuzzy logic and arithmetic of fuzzy numbers, calculates an “estimation interval” and seems to be an adequate mathematical tool for this nonlinear problem. For this model, fuzzy c-means clustering is used to partition data, hence the membership functions and rules are built. In addition, interval arithmetic is used to obtain the fuzzy intervals. The studied sets are different examples of pollution by different anthropogenic sources, in two different study areas: (a) soil samples collected in Antarctica and (b) road-deposited sediments collected in Argentina. The datasets comprise magnetic and chemical variables, and for both cases, relevant variables were selected: magnetic concentration-dependent variables, magnetic features-dependent variables and one chemical variable. The model output gives an estimation interval; its width depends on the data density, for the measured values. The results show not only satisfactory agreement between the estimation interval and data, but also provide valued information from the rules analysis that allows understanding the magnetic behaviour of the studied variables under different conditions.     

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      

边坡稳定性预测的模糊神经网络模型

根据边坡稳定问题具有的模糊性,提出了一种判定边坡稳定性的模糊神经网络模型。该系统仅从期望输入输出数据集即可达到获取知识、确定模糊初始规则基的目的。再利用神经网络学习能力便不难修改规则库中的模糊规则以及隶属函数和网络权值等参数,这样大大减少了规则匹配过程,加快了推理速度,从而极大程度地提高了系统的自适应能力。最后用收集到的边坡数据样本训练和测试模糊神经网络模型,结果表明该模糊神经网络预测边坡稳定性是可行的、有效的。     

Seismic liquefaction potential assessed by fuzzy comprehensive evaluation method

Liquefaction of loose, saturated granular soils during earthquakes poses a major hazard in many regions of the world. The determination of liquefaction potential of soils induced by earthquake is a major concern and an essential criterion in the design process of the civil engineering structures. A large number of factors that affect the occurrence of liquefaction during earthquake exist a form of uncertainty of non-statistical nature. Fuzzy systems are used to handle uncertainty from the data that cannot be handled by classical methods. It uses the fuzzy set to represent a suitable mathematical tool for modeling of imprecision and vagueness. The pattern classification of fuzzy classifiers provides a means to extract fuzzy rules for information mining that leads to comprehensible method for knowledge extraction from various information sources. Therefore, it is necessary to handle the soil liquefaction problem in a rational framework of fuzzy set theory. This study investigates the feasibility of using fuzzy comprehensive evaluation model for predicting soil liquefaction during earthquake. In the fuzzy comprehensive evaluation model of soil liquefaction, the following factors, such as earthquake intensity, standard penetration number, mean diameter and groundwater table, are selected as the evaluating indices. The results show that the method is a useful tool to assess the potential of soil liquefaction.     

基于GIS和单元簇的模糊逻辑推理及其在区域矿产预测中的应用

论述了 GIS支持下的一种基于“单元簇”概念和模糊逻辑推理的多元地学信息综合分析方法及其在区域矿产预测中的应用。针对以往矿产定量预测中的单元划分方法对空间信息利用不足的问题 ,用单元的空间组合 (“单元簇”)代替单元作为定量类比的基本单位 ,从而能较充分地利用地质变量的局部空间结构信息 ;将单元作为 GIS区图元 ,利用 GIS空间分析功能实现对单元及单元簇的管理和操作 ;建立两个层次即变量对单元和组成单元对单元簇的模糊推理规则 ,经两次模糊推理计算出所有未知单元的找矿有利度 ,为进一步圈定找矿远景区提供了基础 ,并以新疆康古尔塔格地区金矿预测实例说明了其应用效果     

计算机辅助地质填图属性数据采集子系统的 动态数据模型

对于复杂的地质属性数据，使用常规的数据库数据模型，需要多界面录入数据，要耗费大量时间在界面的切换上，培训使用系统困难，另外，系统完成封装后，模型和界面就被固定，无法做必要的实时扩充或更改，使用数据字典技术，数据逆向规范化技术和数据挖掘技术支持下的动态数据模型，并把数据录入模型与数据存储模型分离可以很好地解决以上问题，提高了系统的实用性，适应性和生命力。     

基于面向服务体系结构（SOA）的钾盐资源潜力系统

为了对中国钾盐矿产资源潜力进行全面预测,建立了全国性的钾盐矿产资源潜力预测系统。对收集的钾盐信息进行系统地整理,在Oracle空间数据库中建立面向对象的空间数据模型,基于面向服务体系结构进行系统的框架设计,将属性数据和空间数据的操作发布为Web服务,客户端通过网络调用服务实现空间与属性数据的一体化管理及分析,采用面向对象语言C#和ArcObjects二次开发具体实现本系统。系统可通过Web服务与其他地学系统有机集成,对地学数据基于服务集成与共享及一站式服务具有重要参考价值。     

Application of <Emphasis Type="Italic">fuzzy logic and data mining</Emphasis> techniques as tools for qualitative interpretation of acid mine drainage processes

In this article, a set of clustering algorithms based on Fuzzy Logic and Data Mining are applied, allowing to obtain data in the form of linguistic rules and charts about the behaviour of the Tinto and Odiel river estuary (SW Spain) affected by Acid Mine Drainage (AMD). In order to provide researchers with no skills on data mining techniques an easy and intuitive interpretation, we have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data from the estuary water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics.     

一种面向LUCC的时空数据存储管理模型

为满足土地利用和地表覆盖变化(land-use and land-cover change,LUCC)动态监测中时空数据统计分析、时空过程模拟、时空演变分析预测、时空数据挖掘的应用需求,需将LUCC数据存储管理方式由单一空间数据推进至时空数据存储管理。本文提出一种基于快照和面向对象的混合时空数据模型,支持矢量和栅格时空数据存储管理、LUCC时空过程模拟、LUCC时空过程建模参数提取和成果数据管理与发布。基于该模型研发了LUCC时空信息系统原型,以LUCC时空信息统计、地表覆盖斑块时空过程模拟、地表覆盖时空转移矩阵计算为例,验证了模型的可行性。     

人工冻结黏土广义开尔文蠕变本构模型模糊随机优化

冻土的蠕变本构模型是冻结法建井的重要参考资料。在-5℃、-10℃、-15℃和-20℃下,针对人工冻结黏土进行不同应力等级的单轴蠕变试验,获得不同温度、不同应力等级条件下蠕变曲线的变化规律。通过模糊随机迭代搜索改进传统的Guess-Newton算法,得到模糊随机Guess-Newton算法步骤,利用改进的算法优化广义开尔文本构模型参数,获得各温度和应力下的优化蠕变模型。工程试验表明:模糊随机Guess-Newton算法能有效地优化广义开尔文本构模型参数,使模型更好地拟合冻土各阶段的蠕变值,准确地表征了冻土的蠕变特性,同时改进后的算法较传统的算法效率更高,收敛更快。      

基于GeoDatabase的中国岩石圈三维结构数据库设计

数据对于GIS是非常重要的,但目前在各领域的GIS应用中,普遍对数据的重视程度不够。“中国岩石圈三维结构数据库”是基于GIS（地理信息系统）平台下建立起标准、规范的空间数据库,是整个数据库系统稳定运行的基础。CASE工具是建立复杂地理数据库的最佳方法之．,本文依据面向对象的地理数据模型——GeoDatabase,讨论用CASE工具来建立基于GeoDatabase的中国岩石圈三维结构数据库的具体过程。     

FRFI model application in groundwater non-point source pollution evaluation: a case study in the Luoyang Basin of North Henan province, China

The traditional non-point source (NPS) pollution models mainly focus on the flow path of NPS pollutants and attenuation during the flow. Extensive data set preparation and complex results analysis for these models are the most common problems encountered by the model user. In this study a new model, fuzzy-rough sets and fuzzy inference (FRFI), was introduced to evaluate groundwater NPS pollution. The proposed model involves two steps: the algorithm of fuzzy-rough sets attribute reduction (FRSAR) was applied to yield minimal decision rules from the fuzzy information system (FIS); the fuzzy inference technique was then used to forecast a groundwater synthesis pollution index based on the minimal decision rules. This model was applied in the Luoyang Basin, examining NPS pollution factors and hydrochemical variables data to validate the effectiveness of this model. The results indicate that it is only required to collect five NPS pollution factors or three hydrochemical variables; the groundwater synthesis pollution index can be predicted using the FRFI model. The prediction error is restricted to 2.9–6.1 % and 0.8–1.6 %, respectively. Therefore, the costs of computation and monitoring can be decreased, and the user is not required to prepare massive model parameters for the FRFI model. According to analyze the correlation between NPS pollution factors and hydrochemical variables, prevention measures are provided for treatment of the endemic disease and eutrophication. The FRFI model can be suitable for groundwater NPS pollution evaluation systems.