Department of Civil Engineering, University of Calgary, Alberta, Canada.
Integr Environ Assess Manag. 2010 Oct;6(4):711-24. doi: 10.1002/ieam.72.
Different types of uncertain information-linguistic, probabilistic, and possibilistic-exist in site characterization. Their representation and propagation significantly influence the management of contaminated sites. In the absence of a framework with which to properly represent and integrate these quantitative and qualitative inputs together, decision makers cannot fully take advantage of the available and necessary information to identify all the plausible alternatives. A systematic methodology was developed in the present work to incorporate linguistic, probabilistic, and possibilistic information into the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), a subgroup of Multi-Criteria Decision Analysis (MCDA) methods for ranking contaminated sites. The identification of criteria based on the paradigm of comparative risk assessment provides a rationale for risk-based prioritization. Uncertain linguistic, probabilistic, and possibilistic information identified in characterizing contaminated sites can be properly represented as numerical values, intervals, probability distributions, and fuzzy sets or possibility distributions, and linguistic variables according to their nature. These different kinds of representation are first transformed into a 2-tuple linguistic representation domain. The propagation of hybrid uncertainties is then carried out in the same domain. This methodology can use the original site information directly as much as possible. The case study shows that this systematic methodology provides more reasonable results.
在场地特征描述中存在着不同类型的不确定信息——语言的、概率的和可能的。它们的表示和传播极大地影响了受污染场地的管理。在缺乏一个适当的框架来共同表示和整合这些定量和定性输入的情况下,决策者无法充分利用现有和必要的信息来识别所有可能的替代方案。本工作中开发了一种系统的方法,将语言、概率和可能的信息纳入偏好排序组织 enrichment 评估方法(PROMETHEE)中,这是多准则决策分析(MCDA)方法的一个子集,用于对受污染场地进行排序。基于比较风险评估范式的标准识别为基于风险的优先级排序提供了依据。在描述受污染场地时确定的语言、概率和可能的不确定信息可以根据其性质正确地表示为数值、区间、概率分布、模糊集或可能性分布以及语言变量。这些不同类型的表示首先被转换为一个 2-元语言表示域。然后在同一域中进行混合不确定性的传播。该方法可以尽可能多地直接使用原始场地信息。案例研究表明,这种系统的方法提供了更合理的结果。