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D. Sheeren S. Mustière J.‐D. Zucker 《International journal of geographical information science》2013,27(8):961-992
When different spatial databases are combined, an important issue is the identification of inconsistencies between data. Quite often, representations of the same geographical entities in databases are different and reflect different points of view. In order to fully take advantage of these differences when object instances are associated, a key issue is to determine whether the differences are normal, i.e. explained by the database specifications, or if they are due to erroneous or outdated data in one database. In this paper, we propose a knowledge‐based approach to partially automate the consistency assessment between multiple representations of data. The inconsistency detection is viewed as a knowledge‐acquisition problem, the source of knowledge being the data. The consistency assessment is carried out by applying a proposed method called MECO. This method is itself parameterized by some domain knowledge obtained from a second method called MACO. MACO supports two approaches (direct or indirect) to perform the knowledge acquisition using data‐mining techniques. In particular, a supervised learning approach is defined to automate the knowledge acquisition so as to drastically reduce the human‐domain expert's work. Thanks to this approach, the knowledge‐acquisition process is sped up and less expert‐dependent. Training examples are obtained automatically upon completion of the spatial data matching. Knowledge extraction from data following this bottom‐up approach is particularly useful, since the database specifications are generally complex, difficult to analyse, and manually encoded. Such a data‐driven process also sheds some light on the gap between textual specifications and those actually used to produce the data. The methodology is illustrated and experimentally validated by comparing geometrical representations and attribute values of different vector spatial databases. The advantages and limits of such partially automatic approaches are discussed, and some future works are suggested. 相似文献
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变化环境下洪水序列的一致性遭到破坏,引发基于统计原理计算的设计洪水可靠性下降,亟需开展非一致性条件下的设计洪水修订研究.以淮河上游流域为研究区域,运用Pettitt检验法和滑动t检验法综合检测年最大洪峰流量序列突变点,在此基础上,采用SWAT分布式水文模型对变异前的洪峰与洪量序列进行还现,利用径流深的模拟结果修订设计洪水,并对修订后的洪水序列进行频率分析.结果表明:(1)淮河上游息县和淮滨站年最大洪峰流量呈现不显著的减小趋势,王家坝站则表现出不显著增加趋势,1991年为各站年最大洪峰流量序列的突变点;(2) 3个水文站率定期和验证期的确定性系数(R2)和Nash-Sutcliffe效率系数(NSE)均满足适用性要求,其中流域出口王家坝站率定期R2、NSE分别为0.77和0.79、验证期分别为0.72和0.74,模拟精度较高;(3)淮河上游流域洪水设计值较修订前略有减小,其中,洪峰流量减小幅度平均值在3.3%～6.1%之间,淮滨站的减小幅度最大;不同时段洪量的减小幅度平均值在1.4%～2.7%之间,整体修订幅度小于洪峰流量的修订幅度,并且洪量的时段越长,修订幅度越小;随着重现期的增大,各洪水指标的修订幅度逐渐减小.本研究对于变化环境下的淮河流域水利工程规划和防洪减灾具有重要意义. 相似文献
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非一致性时移地震资料叠前互约束处理技术 总被引：1，自引：0，他引：1
时差、相位、频率、振幅、能量差异是影响时移地震处理的主要因素,对于非一致性时移地震资料的处理,消除两期资料在这四个方面的差异是处理工作的关键。笔者首先分析了永新三维两期资料的施工参数差异,在此基础上,利用面元对应抽稀法消除了野外采集观测系统差异;利用有针对性的去噪技术和能量补偿方法,消除了两期资料能量信噪比差异;利用频率相位校正技术,消除了两期资料频率、相位差异。通过这四个方面的互约束处理,取得了较好的处理效果,达到了时移地震资料的处理要求,为后续的时移地震属性分析工作奠定了基础。 相似文献
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