A probabilistic reasoning-based decision support system for selection of remediation technologies for petroleum-contaminated sites

被引：42
|
作者：
He, L
Chan, CW [1 ]
Huang, GH
Zeng, GM
机构：
[1] Univ Regina, Fac Engn, Regina, SK S4S 0A2, Canada
[2] Hunan Univ, Coll Environm Sci & Engn, Changsha 410082, Hunan, Peoples R China
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2006年 / 30卷 / 04期
基金：
加拿大自然科学与工程研究理事会;
关键词：
remediation technologies; petroleum contamination; probabilistic reasoning;
D O I：
10.1016/j.eswa.2005.07.038
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Selection of remediation technologies for petroleum-contaminated sites is difficult given the large number of technologies available and inherent uncertainties involved in the selection process. In this paper, we explore the use of an inexact algorithm for probability reasoning for dealing with the uncertainties involved in the problem. By incorporating domain knowledge as well as the stochastic uncertainty, a probabilistic rule-based decision support system (PDSS) has been developed to support the decision making process. The system has been applied to two case studies, in which the best option of remediation technology can be determined according to calculated probability values. In comparison to deterministic and fuzzy decision support systems, the PDSS can provide a recommendation together with a measure on the reliability or degree to which the recommended decision can be trusted. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：783 / 795
页数：13
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