摘要
以南水北调中线工程输水干渠突发污染事故风险为目标,通过污染事故风险源识别,确定突发污染事故的主要风险源;以店北公路桥突发污染事故风险为研究对象,考虑天气、道路状况、车辆响应情况以及驾驶员特征等因素对事故的影响,构建突发污染事故的贝叶斯网络风险模型,根据贝叶斯网络的风险传递和事故原因推理功能,预测事故风险推断主要风险因子。结果表明:中线工程跨渠桥梁突发污染事故概率为0.06%,风险等级为"低",驾驶员判断和车辆响应是导致突发事故发生的最敏感因子,最不利条件下的事故风险概率为0.98%,是推理风险的16倍。运用河北省2011和2012年实测交通流量和交通事故概率数据对本模型进行了验证,结果表明本模型合理。贝叶斯网络能有效评估、预测长距离输水工程突发污染事故的风险概率,对提高输水工程水质安全和风险管理具有一定的指导意义。
In order to analysis the risk of sudden pollution accidents in the main channel of the Middle Route of the South to North Water Transfer Project, the main sources of risk of sudden pollution accidents is identified through the source identification. Taken an example of Dianbei Bridge as the main location where sudden pollution accidents happen, build a Bayesian network risk model for sudden pollution accidents to analysis the influence of risk factors including weather, road condition, vehicle response and the characteristics of drivers. According the function of risk transfer and accident reasoning, the main risk factors can be inferred. The result shows that the probability of sudden pollution accidents occurred at bridges on this water transfer project is 0.06%, and the risk level is "low". Driver judgment and vehicle response are the most sensitive factors leading to sudden accidents. The probability of accident risk is 0.98% under the most unfavorable conditions, which is 16 times the risk of reasoning accident risk. This model is validated by the measured traffic data in 2011 and 2012 in Hebei Province, and indicats that this Bayesian network model is reasonable. The Bayesian network can effectively estimate and predict the risk probability of sudden pollution accidents in long-distance water transfer projects, which has certain guiding significance for improving water quality safety and risk management of water transfer projects.
作者
唐彩红
易雨君
张尚弘
TANG Caihong;YI Yujun;ZHANG Shanghong(Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University,Beijing 100875 , China;Renewable Energy School, North China Electric Power University, Beijing 102206, China)
出处
《水利水电技术》
北大核心
2019年第2期28-34,共7页
Water Resources and Hydropower Engineering
基金
国家自然科学基金(51722901
51439001)
北京师范大学学科交叉建设项目资金资助
关键词
贝叶斯网络
风险分析
水污染
输水工程
Bayesian network
risk assessment
water pollution
water transfer project
