摘要
事故造成的水质突发性风险评估对于河流水质安全管理具有重要意义.通过贝叶斯网络直观地表示事故风险源和河流水质之间的相关性,并用时序蒙特卡罗算法将风险源状态模拟、水质模拟和贝叶斯网络推理过程结合,可以对多个风险源共同影响下的河流突发性水质污染事故的超标风险进行量化评估.案例研究表明,多风险源对同一受纳水体的水质突发性污染事故风险的耦合影响十分显著,在进行流域水安全管理时必须对多风险源进行综合管理.同时,模型的诊断推理功能可为流域关键风险源识别和管理提供决策支持.
Risk assessment for accidental pollution plays an important role in fiver water quality management. Bayesian networks can be applied to represent the relationship between pollution sources and river water quality intuitively. A time sequential Monte Carlo algorithm, integrated with pollution sources model, water quality model and Bayesian reasoning, is developed to quantify river water quality risk under the collective influence of multiple pollution sources. A case study shows that multiple pollution sources have obvious effect on water quality risk of the receiving water body, which means that integrated management should be developed for multiple risk sources. The model could also support the decision-making process of river basin management through identification of critical pollution sources.
出处
《环境科学》
EI
CAS
CSCD
北大核心
2009年第1期47-51,共5页
Environmental Science
关键词
风险评估
贝叶斯网络
时序蒙特卡罗模拟
水质模型
risk assessment
Bayesian networks
time sequential Monte Carlo
water quality model
