A probabilistic framework for representing and simulating uncertain environmental variables
A probabilistic framework for representing and simulating uncertain environmental variables
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DOI：
10.1080/13658810601063951
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发表时间：
2007-05-01
影响因子：
5.7
通讯作者：
van Loon, E. E.
中科院分区：
文献类型：
--
作者：
Heuvelink, G. B. M.;Brown, J. D.;van Loon, E. E.
了解环境数据的局限性对于有效管理环境系统和鼓励负责任地使用不确定数据非常重要。因此，对与环境数据相关的不确定性及其在数据库中的存储进行明确评估是重要的。本文提出了一个表示和模拟不确定环境变量的统计框架。一般而言，不确定变量完全由其概率分布函数(Pdf)来表示。PDF是为位置不确定(位置不确定)和属性值不确定(属性不确定)的对象开发的。由多个时空位置组成的对象被分成位置不确定性不能改变对象内部几何的“刚性对象”和位置不确定性可以在一个对象中的位置之间变化的“变形”对象。在一个对象的多个位置的不确定之间允许统计相关性。与属性值相关的不确定性也用pdf建模。这些pdf的类型和复杂程度取决于测量尺度和不确定属性的时空变异性。并用实例说明了该框架。还提出了一个原型软件工具，用于评估环境数据中的不确定性，将其存储在数据库中，并用于生成用于蒙特卡罗研究的实现。
Understanding the limitations of environmental data is important for managing environmental systems effectively and for encouraging the responsible use of uncertain data. Explicit assessment of the uncertainties associated with environmental data, and their storage in a database, are therefore important. This paper presents a statistical framework for representing and simulating uncertain environmental variables. In general terms, an uncertain variable is completely specified by its probability distribution function (pdf). Pdfs are developed for objects with uncertain locations ('positional uncertainty') and uncertain attribute values ('attribute uncertainty'). Objects comprising multiple space-time locations are separated into 'rigid objects', where positional uncertainty cannot alter the internal geometry of the object, and 'deformable' objects, where positional uncertainty can vary between locations in one object. Statistical dependence is allowed between uncertainties in multiple locations in one object. The uncertainties associated with attribute values are also modelled with pdfs. The type and complexity of these pdfs depend upon the measurement scale and the space-time variability of the uncertain attribute. The framework is illustrated with examples. A prototype software tool for assessing uncertainties in environmental data, storing them within a database, and for generating realizations for use in Monte Carlo studies is also presented.