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Abstract Although the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been widely applied in water quality assessment by numerous studies several common limitations remain unresolved. Specifically: 1) Subjective elements in methods such as fuzzy theory and the analytic hierarchy process (AHP) may distort evaluation outcomes; 2) The utilization of raw sample data is insufficient when constructing evaluation matrices; 3) The traditional entropy weight method in TOPSIS merely reflects statistical characteristics of the final matrix while neglecting richer information embedded in raw datasets. To address these issues we proximate probability distribution function of various indicators by using cubic spline interpolation and fully exploit information in the existing massive sample data. In this paper the entropy weight method is enhanced based on the concept mentioned above and integrated with TOPSIS model to construct a novel evaluation model. Furthermore the experimental analysis using wastewater monitoring data from Guizhou Province China verifies its practicality and its results provide valuable references for local water environmental management. 关键词 Keywords references Ma J Q ， Kerachian R . Revising river water quality monitoring networks using discrete entropy theory: The Jajrood River experience [J ] . Environmental Monitoring and Assessment ， 2013 。
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