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Dynamic assessment of water quality based on a variable fuzzy pattern recognition model.

Shiguo Xu1, Tianxiang Wang2, Suduan Hu3

  • 1Faculty of Infrastructure Engineering, School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China. sgxu@dlut.edu.cn.

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This study introduces a dynamic water quality assessment method using variable fuzzy pattern recognition (VFPR). The approach accurately reflects seasonal changes in water quality, particularly in reservoirs.

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Applied Mathematics

Background:

  • Effective water quality assessment is crucial for water resource protection.
  • Dynamic and fuzzy changes in water quality pose challenges for accurate assessment.
  • Existing methods struggle to fully capture the temporal and inherent uncertainties of water quality indicators.

Purpose of the Study:

  • To develop a novel method for dynamic and fuzzy water quality assessment.
  • To address the limitations of traditional assessment models in reflecting real-time water quality fluctuations.
  • To improve the accuracy and reliability of water quality evaluations in complex environments.

Main Methods:

  • Integration of the variable fuzzy pattern recognition (VFPR) model.
  • Combination with the analytic hierarchy process (AHP) for indicator weighting.
  • Incorporation of the entropy weight (EW) method to objectively determine indicator importance.
  • Application of the combined model to Biliuhe Reservoir for dynamic assessment.

Main Results:

  • The proposed method successfully assessed water quality dynamically, identifying levels between 2 and 3.
  • Water quality was found to degrade in August and September, linked to increased water temperature and rainfall.
  • Analysis included comparisons of weighting methods and evaluation of random indicator errors.

Conclusions:

  • The developed method offers dynamism, fuzzification, and stability in water quality assessment.
  • It effectively considers the interval influence of multiple indicators.
  • Utilizing average level characteristic values from four models enhances assessment robustness.