基于三角模糊数-贝叶斯方法的九洲江水环境质量评价

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英文篇名：Water Quality Assessment of Jiuzhou River Based on the Integrated Method of Triangular Fuzzy Numbers and Bayesian Network 作者：邓渠成 ; 尹娟 ; 许桂苹 ; 黄玉清 ; 王晓飞 ; 滕云梅 ; 陈洋 ; 夏新建 英文作者：DENG Qu-cheng;YIN Juan;XU Gui-ping;HUANG Yu-qing;WANG Xiao-fei;TENG Yun-mei;CHEN Yang;XIA Xin-jian;School of Earth and Environmental Science,University of Queensland;Department of Management Science and Engineering,Guangxi University of Finance and Economics;College of Light Industry and Food Engineering,Guangxi University;Environmental Monitoring Station of Guangxi;Key laboratory of Environmental Change and Resources Use in Beibu Gulf,Guangxi Teachers Education University; 关键词：三角模糊数 ; 贝叶斯 ; 九洲江 ; 水质评价 英文关键词：triangular fuzzy numbers;;Bayesian theory;;Jiuzhou River;;water quality assessment 中文刊名：SCAN 英文刊名：Journal of Hydroecology 机构：澳大利亚昆士兰大学地球与环境科学学院;广西财经学院管理科学与工程学院;广西大学轻工与食品工程学院;广西壮族自治区环境监测中心站;北部湾环境演变与资源利用教育部重点实验室广西师范学院; 出版日期：2019-03-15 出版单位：水生态学杂志 年：2019 期：v.40 基金：广西社会科学重点课题(GXSK201605);; 广西科技计划项目(1598014-3);; 广西自然科学基金重大项目(2013GXNSFEA053001) 语种：中文; 页：SCAN201902003 页数：6 CN：02 ISSN：42-1785/X 分类号：16-21

摘要

为更为全面考察水质评价过程中的不确定性,将模糊集理论和贝叶斯理论引入河流水质评价中,建立了三角模糊数-贝叶斯方法的水环境质量评价模型,并选取2016年7月九洲江跨省交界处5个断面的溶解氧、生化需氧量、高锰酸盐指数、氨氮、总磷作为水质评价因子,将该模型应用于九洲江跨省段的水质评价中。结果表明,九洲江文车桥、圭地河下游、温水浪、山角、石角蟠龙桥断面水质区间数分别为[3.320,3.365]、[3.120,3.179]、[3.178,3.196]、[3.432,3.428]、[3.404,3.470],均在[3,4]区间范围,为III类水质;断面水质优劣排序为:石角蟠龙桥>山角>文车桥>温水浪>圭地河下游。该模型与综合污染指数法相比,断面水质优劣排序情况一致,除圭地河下游断面外,其他断面的水质评价结果也一致。由于模型计算过程中考虑了历史先验信息,并采用了三角模糊数及区间数的结果表达方式,有利于更客观反映流域水质状况。
        With increasing economic development and population growth in China, water pollution, water security and shortage of water resources have become issues of concern. To date, few studies have addressed both the uncertainties of model structure and parameterization when assessing water quality. As a result, the accuracy and comprehensiveness of the assessment results is compromised. This study aimed to investigate the uncertainties in the process of water quality assessment. An integrated fuzzy Bayesian water quality evaluation model has been developed and the fuzzy set and Bayesian analysis were introduced in this study. Water quality parameters included in the assessment included dissolved oxygen(DO), biochemical oxygen demand(BOD_5), permanganate index(COD_(Mn)), ammonia nitrogen(NH_3-N) and total phosphorus(TP). Approximately 30 water samples were collected from the Jiuzhou River within the trans-provincial basin areas in July 2016. The integrated fuzzy Bayesian water quality evaluation model was then used to evaluate water quality based on the monitoring results. The water quality interval numbers at Wenche Bridge, Guidi River 1 000 m downstream, Wenshuilang, Shanjiao and Shijiaopanlong Bridge monitoring sites were, respectively, [3.320, 3.365], [3.120, 3.179], [3.178, 3.196], [3.432, 3.428] and [3.404, 3.470]. Water quality in all sections was Class III according to water quality evaluation standards. The order of sections, in order of water quality was Shijiaopanlong Bridge>Shanjiao>Wenche Bridge>Wenshuilang>Guidi River 1 000 m downstream. Compared with water quality evaluation results based on the comprehensive pollution index, ranking was the same and the water quality classification was consistent for all monitoring sections, except for Guidi River 1 000 m downstream. In conclusion, the triangular fuzzy number model of assessment was shown to have a strong model structure and mitigated model parameter uncertainties. The assessment results obtained using this method are therefore more objective for evaluating water quality.

引文

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