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浙江大学学报（农业与生命科学版）
浙江大学学报(农业与生命科学版)  2012, Vol. 38 Issue (6): 747-754    DOI: 10.3785/j.issn.1008-9209.2011.11.141
资源与环境科学     
基于粗糙集和证据理论的水质分析预警技术研究
朱琼瑶， 张光新[1]， 冯天恒， 黄平捷， 侯迪波  
浙江大学 控制科学与工程学系/工业控制技术国家重点实验室，浙江 杭州 310027
Study on water quality analysis and early-warning technology based on rough set and evidence theory
ZHU Qiong-yao, ZHANG Guang-xin*, FENG Tian-heng, HUANG Ping-jie, HOU Di-bo
State Key Laboratory of Industrial Control Technology / Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
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摘要： 针对水质检测监测和信息管理系统中监测指标种类较多、水质指标之间往往存在内在关联、水质变化规律隐含较深、水质分析预警准确度难以保证的问题，研究和分析了粗糙集和Dempster-Shafer (D-S) 证据理论，提出了基于粗糙集和D-S证据理论联合应用的水质预警算法. 结果表明：该方法降低了实际工作中存在的水质历史数 据缺失、冲突和冗余问题以及数据量偏少对分析预警结果的影响，有效减少了预警分析的计算量；通过实际数据，以高锰酸盐指数 (CODMn) 指标分析预测为例，对比分析了单属性拟合预测、基于粗糙集理论的趋势预测、粗糙集和D-S证据理论联合分析预测3种方法的优点和不足，验证了本研究方法是有效的.
Abstract:   With the development and progress of society, the problem of water environment pollution has become one of the topics which are cared and taken seriously by people. Higher requirements are brought forward to come up with the frequent and various water pollution events. The current used monitoring systems are difficult to meet the higher requirements referred before. How to monitor water quality effectively and to analyze the concentration of contaminants in water qualitatively during or after water pollution events have become arduous tasks for a water quality monitoring system. As it is known to all, there are lots of monitoring parameters in water quality detection, monitoring and information management system, and the evolution principles and changing trends of water quality are difficult to be obtained. The accuracy of water quality analysis and early-warning is hard to guarantee. Aiming to treat these problems, a new method for a water monitoring system is needed to study on. In this study, the rough set and Dempster-Shafer (D-S) evidence theories were researched, and the water quality analysis and early-warning algorithm based on rough set and D-S evidence theories were presented. The original monitoring water quality data was given (Table 1). The discretization in this paper was based on GB3838-2002, and the result of discretization was also given (Table 3). After discretization, the incomplete data and the conflicting data were deleted (Table 3). Moreover, in order to solve the problem of redundancy parameters, the rough set was used. After these steps, a minimal data list was acquired. Then, the data in the list was recovered to take the place of discrete value. Based on the function of basic probability assignment and the D-S rule of combination, the probability was assigned and the data was fused (Table 4), and the result of data fusion could reflect the water quality or the concentration of contaminants in the water qualitatively. Refer to the national standard, the value of CODMn and the water quality in different time points were acquired (Fig. 4, 5). The analysis results which were based on multistage function fitting and rough set theory were also given so as to compare with the result based on rough set and D-S evidence theories (Fig. 2-3). Through the results of analysis, the method based on rough set and D-S evidence theories was proved to be feasible and well. Comparing to the result based on rough set and D-S evidence theories, the result based on multistage function fitting had larger deviation with the passage of time, and the result based on rough set theory had the case which could not be inferred by historical data. In conclusion, the method based on rough set and D-S evidence theories can reduce the influence of historical water data losses, conflicts and redundancy, and overcome the data shortage in practical systems. The reduction of water monitoring parameters which is based on rough set theory also reduces the calculation cost dramatically
出版日期: 2012-11-20
基金资助:

国家重大科技专项资助项目 (2008ZX07420-004)；国家自然科学基金资助项目 (41101508).
通讯作者: 张光新，E-mail: gxzhang@zju.edu.cn   
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引用本文:

朱琼瑶， 张光新,冯天恒， 黄平捷， 侯迪波. 基于粗糙集和证据理论的水质分析预警技术研究[J]. 浙江大学学报(农业与生命科学版), 2012, 38(6): 747-754.

ZHU Qiong-yao, ZHANG Guang-xin*, FENG Tian-heng, HUANG Ping-jie, HOU Di-bo. Study on water quality analysis and early-warning technology based on rough set and evidence theory. , 2012, 38(6): 747-754.

链接本文:

http://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2011.11.141    或    http://www.zjujournals.com/agr/CN/Y2012/V38/I6/747

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