Water Quality Evaluation Method Based on a T-S Fuzzy Neural Network—Application in Water Environment Trend Analysis of Taihu Lake Basin

Wei Ye,Chen-Feng Cui,Jia-Hao Wen,Wei Song

doi:10.3390/w13213127

Abstract

In response to the problems of large computational volume and tedious computational process of fuzzy integrated evaluation, and general neural network models without clear water quality training criteria, this paper organically combines fuzzy rules, affiliation function, and neural network, and proposes a comprehensive method for the evaluation of water quality based on a T-S fuzzy neural network. On the three water quality monitoring data of six national key monitoring stations in Taihu Lake Basin, three evaluation methods—the one-factor evaluation method, the fuzzy integrated evaluation method, and the T-S fuzzy neural network evaluation method—were used to comprehensively evaluate water environment quality, and the results showed that the T-S fuzzy neural network method has the advantages of convenient calculation, strong applicability, and scientific results.

Highlights

Water Quality Evaluation Results Based on One-Factor Evaluation
As one of the more successful methods used in the evaluation of water quality, the fuzzy integrated evaluation method takes into account both the fuzzy and hierarchical nature of the evaluation object, so that qualitative and quantitative evaluation can be combined to expand the amount of information on the evaluation object and improve the evaluation accuracy
The organic combination of fuzzy rules, affiliation functions, and neural networks complement one another’s strengths and weaknesses; fuzzy rules give the model a strong logical reasoning ability, grant it the ability to deal with higher order problems, and at the same time enable it to solve the accurate information and some uncertainty of fuzzy information

Summary

Introduction

How to accurately evaluate the degree of water pollution, identify the evolution pattern of the water environmental system, and propose scientific and effective water environmental protection measures are the main tasks of managers and scientific researchers [1]. Puckett et al used principal component analysis to evaluate the water quality of some rivers in Virginia, USA, and obtained the main pollution factors from many water quality monitoring data in 2004 [3]. Noori et al (2011) investigated the use of gamma tests and forward selection techniques to evaluate monthly river flow predictions, and concluded that correlation analysis can deal well with the relationship between physical and chemical parameters [5].

Objectives

Methods

Results

Discussion

Conclusion