scholarly journals Research on application of ReliefF and improved RVM in water quality grade evaluation

2022 ◽  
Author(s):  
Yu-Lin Gong ◽  
Ming-Jia Hu ◽  
Hui-Fang Yang ◽  
Bo Han
Keyword(s):  
Water Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Threshold Value ◽  
Pso Algorithm ◽  
Relevance Vector Machine ◽  
Water Quality Evaluation ◽  
Quality Grade ◽  
Factor Combination ◽  
Research On Application

Abstract ReliefF algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to build a water quality evaluation model, and the experimental results of RVM, PSO-RVM, ReliefF-RVM and PSO-ReliefF-RVM were compared. The results show that ReliefF algorithm, combined with threshold value, selects 5 evaluation factors with significant weight from 8 evaluation factors, which reduces the amount of data used in the model, CSI index is used to calculate the separability of each evaluation factor combination. The results show that the overall separability of the combination is best when the evaluation factor with significant weight is reserved. When different water quality evaluation factors were included, the evaluation accuracy of PSO-ReliefF-RVM model reached 95.74%, 14.23% higher than that of RVM model, which verified the effectiveness of PSO algorithm and ReliefF algorithm, and had a higher guiding significance for the study of water quality grade evaluation. It has good practical application value.

TOPSIS Evaluation Model of Surface Water Quality Based on Normalized Index Values

2012 ◽  
Vol 195-196 ◽  
pp. 1232-1236
Author(s):  
Jia Yang Wang ◽  
Chun Xue Yu ◽  
Zuo Yong Li ◽  
Wen Sheng Wang
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Reference Values ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Surface Water Quality ◽  
Water Quality Evaluation

By setting up the reference values and the normalized transformation forms for multiple indexes of the surface water, the TOPSIS evaluation model of surface water quality based on normalized indexes was proposed. It was used in water quality evaluation cases about 6 monitoring sections of Li River form 2008 to 2009, and the results show that the model is practical and universal.

scholarly journals Incorporating pollutants interaction with the environment and parameter uncertainty in water quality evaluation: a case of Lake Chauhan, China

2017 ◽  
Vol 18 (2) ◽  
pp. 723-736 ◽  
Author(s):  
Jingneng Ni ◽  
Jiuping Xu ◽  
Mengxiang Zhang
Keyword(s):  
Water Quality ◽  
Parameter Uncertainty ◽  
Water Resource Management ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Water Environment ◽  
Management Decision ◽  
Water Quality Evaluation ◽  
New Approach

Abstract Water quality evaluation is a key task in water resource management and pollution control. Current evaluation methods are rooted in water quality index, which assesses the water quality based on the exact concentration of various pollutants. However, the interaction between the pollutants and the water environment should also be considered. This paper suggests a new approach, which integrates pollutant interaction with water environment and parameter uncertainty to water quality evaluation. The new approach is compared with traditional methods. Then, an inexact evaluation model, the integrated water quality evaluation model under uncertainty, is established in accordance with the proposed approach, in which catastrophe theory is used to deal with the ambiguous internal mechanism of the interaction between the pollutants and the water environment. As there are significant uncertainties in water quality evaluations, fuzzy random variables are employed to describe the inexact monitoring data. To solve the proposed model, a new algorithm is designed. The model is then applied to an actual case: Lake Chaohu, China. The results are compared between the proposed method and China's current evaluation method (i.e. max-index method). Some brief analysis and discussion are given about the results, which could be helpful in guiding environmental management decision-making.

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