Dynamic water quality evaluation based on fuzzy matter–element model and functional data analysis, a case study in Poyang Lake

Abstract

Comprehensively evaluating water quality with a single method alone is challenging because water quality evaluation involves complex, uncertain, and fuzzy processes. Moreover, water quality evaluation is limited by finite water quality monitoring that can only represent water quality conditions at certain time points. Thus, the present study proposed a dynamic fuzzy matter–element model (D–FME) to comprehensively and continuously evaluate water quality status. D–FME was first constructed by introducing functional data analysis (FDA) theory into a fuzzy matter–element model and then validated using monthly water quality data for the Poyang Lake outlet (Hukou) from 2011 to 2012. Results showed that the finite water quality indicators were represented as dynamic functional curves despite missing values and irregular sampling time. The water quality rank feature curve was integrated by the D–FME model and revealed comprehensive and continuous variations in water quality. The water quality in Hukou showed remarkable seasonal variations, with the best water quality in summer and worst water quality in winter. These trends were significantly correlated with water level fluctuations (R = −0.71, p < 0.01). Moreover, the extension weight curves of key indicators indicated that total nitrogen and total phosphorus were the most important pollutants that influence the water quality of the Poyang Lake outlet. The proposed D–FME model can obtain scientific and intuitive results. Moreover, the D–FME model is not restricted to water quality evaluation and can be readily applied to other areas with similar problems.

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