Abstract
In this paper, an improved fuzzy matter-element (IFME) method was proposed, which integrates the classical matter-element (ME) method, set pair analysis (SPA), and variable coefficient method (VCM). The method was applied to evaluate water quality of five monitor stations along Caoqiao River in Yixing city, Jiangsu Province, China. The levels of river water quality were determined according to fuzzy closeness degree. Compared with the traditional evaluation methods, the IFME method has several characteristics as follows: (i) weights were determined by the VCM method, which can reduce workload and overcome the adverse effects of abnormal values, (ii) membership degrees were defined by SPA, which can utilize monitored data more scientifically and comprehensively, and (iii) IFME is more suitable for seriously polluted rivers. Overall, these findings reinforce the notion that an integrated approach is essential for attaining scientific and objective assessment of river water quality.
Highlights
River water pollution is one of the most widespread environmental issues in the 21st century [1,2,3].Since rivers carry off domestic sewage, industrial wastewater, and agricultural discharge, as well as serve as vital water sources for populations, irrigation, industry, and other applications, it plays significant roles in the economic development of watersheds [4,5]
Method has several characteristics as follows: (i) weights were determined by the variation coefficient method (VCM) method, which can reduce workload and overcome the adverse effects of abnormal values, (ii) membership degrees were defined by set pair analysis (SPA), which can utilize monitored data more scientifically and comprehensively, and (iii) improved fuzzy matter-element (IFME) is more suitable for seriously polluted rivers
The IFME model was established based on water quality data from five monitoring stations along the Caoqiao River
Summary
River water pollution is one of the most widespread environmental issues in the 21st century [1,2,3].Since rivers carry off domestic sewage, industrial wastewater, and agricultural discharge, as well as serve as vital water sources for populations, irrigation, industry, and other applications, it plays significant roles in the economic development of watersheds [4,5]. A rising number of mathematical approaches have been extensively developed to evaluate water quality, such as multivariate statistical analysis (MVSA) [4,9,10,11,12,13,14], artificial neural networks [15,16], fuzzy comprehensive assessments [17,18], matter-element analysis [6], etc. These mathematical assessments methods have been widely applied for researchers to help solve problems in water-related environmental management. Improving the disadvantages of each method and combining the advantages of various methods are indispensable in comprehensive assessments [9,19]
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