基于贝叶斯网络拓扑结构的水环境风险溯源——以饮马河流域为例.pdf

中国环境科学 2022,42(5)：2299~2304 China Environmental Science
addition, Bayesian
network topology analysis and heuristic search algorithm can quickly identify typical pollutant sources and their pollution
contributions in the watershed. In this study, the monitoring data of Drinking Horse River Basin in Jilin Province from 2017 to 2020
were selected for the water quality analysis. Ammonia was a typical pollutant in the watershed; the three sections of Khao San Nan
Lou, Khao San Bridge and Liu Zhen Tun were polluted by Yang Jia Weizi, Xin Li Cheng Dam and Zhuang Wa Yao Bridge
respectively. 63% of the pollution in Khao San Lou came from Yangjia Weizi, 30% of the pollution in Khao San Qiao came from
Xinlizheng Dam, and 75% of the pollution in Liu Zhen Tun came from Brick Wayao Bridge. This assessment method can be
constructed to provide strong technical support for the tracing of water environment risk and pollution responsibility determination in
the basin.
Key words：water pollution；risk traceability；mutual information；Bayesian network；heuristic search；yinma river basin

[7]
随着经济社会的进步,我国流域水环境问题日 型法具体分为确定性方法和数理统计方法 .通过
趋严