Water environmental risk tracing based on the combination of Bayesian network topology:A case study of Yinma River Basin
WANG Ze-zheng1,2, ZHANG Shuai3, WANG Li-min3, ZHANG Wen-jing1,2, DU Shang-hai1,2
1. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China; 2. College of New Energy and Environment, Jilin University, Changchun 130021, China; 3. School of Computer Science, Jilin University, Changchun 130021, China
Abstract：In order to solve the problems of unclear pollution sources and difficult to quantify pollution contribution in the process of watershed water environment risk diagnosis, an accountability quantification method of pollution sources based on the combination of Bayesian network topology and heuristic search algorithm was proposed in this paper. The method can accurately identify typical pollutants in watershed water environment according to the quantitative evaluation of mutual information. In 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.
王泽正, 张帅, 王利民, 张文静, 杜尚海. 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[J]. 中国环境科学, 2022, 42(5): 2299-2304.
WANG Ze-zheng, ZHANG Shuai, WANG Li-min, ZHANG Wen-jing, DU Shang-hai. Water environmental risk tracing based on the combination of Bayesian network topology:A case study of Yinma River Basin. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(5): 2299-2304.
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