Improvement of CCME WQI using grey relational method

Feng, Yujie; Qiao, Danying; Bao, Qing; Ma, Lin; Xing, Xigang; Zhang, You; Wang, Xiaogang

doi:10.1016/j.jhydrol.2016.10.007

Cited by 56 publications

(18 citation statements)

References 13 publications

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“…Compared with various subjective weighting models, the biggest advantage of the EWM is the avoidance of the interference of human factors on the weight of indicators, thus enhancing the objectivity of the comprehensive evaluation results [3,4]. erefore, the EWM has been widely used in decision-making in recent years [5][6][7]. For example, Wu et al [8] made a comprehensive assessment on lake water quality in Shahu Lake to provide valuable information about present lake water quality for decisionmaking [8].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Entropy Weight Method in Decision-Making

Zhu

Tian²,

Feng

2020

Mathematical Problems in Engineering

Self Cite

315

145

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Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.

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Section: Introductionmentioning

confidence: 99%

Effectiveness of Entropy Weight Method in Decision-Making

Zhu

Tian²,

Feng

2020

Mathematical Problems in Engineering

Self Cite

315

145

View full text Add to dashboard Cite

show abstract

“…), but few of them reported that they directly used the formula for water quality assessment (CCMEWQI) [20]. Furthermore, the WQI method has been applied for different purposes, but mainly for surface water quality (especially for river water) [21][22][23][24][25][26][27][28][29], groundwater quality [1,[30][31][32][33][34][35][36], and wetland [37][38][39][40][41][42] across the world. Moreover, the Environmental Protection Agency (EPA) of the Republic of Serbia also utilizes WQI to inform about the overall status of the river water system [43].…”

Section: Introductionmentioning

confidence: 99%

Modification of the Water Quality Index (WQI) Process for Simple Calculation Using the Multi-Criteria Decision-Making (MCDM) Method: A Review

Akhtar

Ishak

Ahmad

et al. 2021

Water

167

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Human activities continue to affect our water quality; it remains a major problem worldwide (particularly concerning freshwater and human consumption). A critical water quality index (WQI) method has been used to determine the overall water quality status of surface water and groundwater systems globally since the 1960s. WQI follows four steps: parameter selection, sub-indices, establishing weights, and final index aggregation, which are addressed in this review. However, the WQI method is a prolonged process and applied to specific water quality parameters, i.e., water consumption (particular area and time) and other purposes. Therefore, this review discusses the WQI method in simple steps, for water quality assessment, based on two multi-criteria decision-making (MCDM) methods: (1) analytical hierarchical process (AHP); and (2) measuring attractiveness by a categorically based evaluation technique (MACBETH). MCDM methods can facilitate easy calculations, with less effort and great accuracy. Moreover, the uncertainty and eclipsing problems are also discussed—a challenge at every step of WQI development, particularly for parameter selection and establishing weights. This review will help provide water management authorities with useful knowledge pertaining to water usage or modification of existing indicators globally, and contribute to future WQI planning and studies for drinking, irrigation, domestic, and industrial purposes.

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“…The traditional Grey Relational Degree Method is relatively simple in calculation. However, when the pollution factors are significantly different, the average value with equal weights would understate the pollution factor with high concentration while overstate the pollution factor with low concentration, which would differ from the actual pollution condition [49][50][51]. The improved method above determines different air quality levels based on the observed concentration of different pollution factors, and calculates the weights and correlation coefficients of each pollution factor accordingly.…”

Section: The Improved Grey Relational Degree Methodsmentioning

confidence: 99%

Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Yan

Sun

et al. 2019

Sustainability

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In recent years, China's urban air pollution has caused widespread concern in the academic world. As one of China's economic and financial centers and one of the most densely populated cities, Shanghai ranks among the top in China in terms of per capita energy consumption per unit area. Based on the Shanghai Energy Statistical Yearbook and Shanghai Air Pollution Statistics, we have systematically analyzed Shanghai’s atmospheric pollutants from three aspects: Primary pollutants, pollutants changing trends, and fine particulate matter. The comprehensive pollution index analysis method, the grey correlation analysis method, and the Euclid approach degree method are used to evaluate and analyze the air quality in Shanghai. The results have shown that Shanghai's primary pollutants are PM2.5 and O3, and the most serious air pollution happens during the first half of the year, particularly in the winter. This is because it is the peak period of industrial energy use, and residential heating will also lead to an increase in energy consumption. Furthermore, by studying the particulate pollutants of PM2.5 and PM10, we clearly disclosed the linear correlation between PM2.5 and PM10 concentrations in Shanghai which varies seasonally.

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Improvement of CCME WQI using grey relational method

Cited by 56 publications

References 13 publications

Effectiveness of Entropy Weight Method in Decision-Making

Effectiveness of Entropy Weight Method in Decision-Making

Modification of the Water Quality Index (WQI) Process for Simple Calculation Using the Multi-Criteria Decision-Making (MCDM) Method: A Review

Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

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