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Environmental science and pollution research international
Mar, 2024;31 (11): 17018-17032.

Optimization of water quality evaluation index using information sensitivity method and variable fuzzy model for the Guo River, China.

Shuoya Cheng, Peigui Liu, Mei Yao, Mei Li, Meng Liu, Manting Shang
Author Information
  1. Shuoya Cheng: School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China.
  2. Peigui Liu: School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China. liupg2512@163.com. ORCID
  3. Mei Yao: Hydrology Bureau of Anhui Province, Hefei, 230022, China.
  4. Mei Li: Hefei Institute for Public Safety Research, Tsinghua University, Hefei, 230601, China.
  5. Meng Liu: Anhui and Huaihe River Institute of Hydraulic Research, Hefei, 230088, China.
  6. Manting Shang: School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China.
PMID: 38329673 DOI: 10.1007/s11356-024-32318-w

Abstract

The more water quality evaluation indicators, the greater the amount of water quality evaluation calculation. Under the requirements of evaluation accuracy, the index screening method is usually used to optimize water quality evaluation index to reduce the calculation amount of water quality evaluation. Taking Mengcheng Gate of Guo River as an example, the information sensitivity index screening method was used to simplify the water quality evaluation index system from 17 to 12 indicators. The variable fuzzy comprehensive evaluation method was used to compare and evaluate the original index system and the optimal index system. The results showed that the water quality results of the optimal index system are consistent with the original index evaluation results. And the water quality of Mengcheng Gate is basically stable at class I level. The information sensitivity method reduced the number of indicators by 29.41%. The error between the water quality evaluation results based on the optimal indicators and the original indicators is less than 10%. The maximum error, minimum error, and average error are 8.92%, 0.08%, and 2.46%, respectively. It revealed that the information sensitivity method can eliminate the information redundancy while retaining the information of the original index system. It can also reduce the calculation amount of water quality evaluation in the future. The variable fuzzy comprehensive evaluation method can reasonably reflect the complex nonlinear relationship between water quality index and water quality category. This accuracy has practical significance for guiding the optimization of water quality evaluation index system, improving the efficiency of water quality evaluation. This model provides a scientific basis for indicator selection methods in similar river water quality evaluations.

Keywords

Fuzzy variables Index weight Indicator selection Information sensitivity Principal component analysis Water quality evaluation

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Grants

  1. 202003a07020010/Major Special Foundation of Science and Technology of Anhui Province
  2. 202203a07020034/Major Special Foundation of Science and Technology of Anhui Province

MeSH Term

Water Quality
Environmental Monitoring
Rivers
Fuzzy Logic
China
Journal Article

Word Cloud

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