A Groundwater Quality Assessment Model for Water Quality Index: Combining Principal Component Analysis, Entropy Weight Method, and Coefficient of Variation Method for Dimensionality Reduction and Weight Optimization, and Its Application. DOI
Beibei Zhang,

Xin Hu,

Bo Li

et al.

Water Environment Research, Journal Year: 2024, Volume and Issue: 96(12)

Published: Dec. 1, 2024

Abstract Groundwater underpins water supply for most of the world's regions, yet its sustainable utilization has been markedly compromised by inappropriate exploitation and a multitude pollution sources. Water quality evaluation emerged as an essential strategy to guarantee optimized vigilant conservation resources. In this study, principal component analysis (PCA), entropy weight method (EWM), coefficient variation (CVM), Quality Index (WQI) were used construct integrated WQI groundwater assessment model that integrates PCA‐CVM‐EWM dimensionality reduction optimization. Taking village in Shandong Province, China, example, PCA identified seven indicators. The CVM‐EWM coupled calculate comprehensive weights through principle minimum information entropy, followed based on values. results indicated Class III predominated study area, accounting 74%, with localized present. hydrochemical type was primarily SO 4 ·HCO 3 ‐Ca, significantly influenced human activities. coefficients Fe, Mn, NH ‐N all exceeded 1. Compared other methods, demonstrated superior performance selection evaluative indicators, distribution, assessment, showing distinct advantage data numerous indicators substantial variation. findings provided scientific reference diagnosing issues formulating preventive control measures. Practitioner Points A index constructed. Optimized steps selecting assigning model. Selection indicator correlation analysis. variability is considered.

Language: Английский

Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis DOI Creative Commons
Zhan Xie,

Weiting Liu,

Si Chen

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102227 - 102227

Published: Feb. 17, 2025

Language: Английский

Citations

2
Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models DOI

Yuming Mo,

Jing Xu,

Chanjuan Liu

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(11)

Published: Oct. 3, 2024

Language: Английский

Citations

4
Evaluation of groundwater quality and health risk assessment in Dawen River Basin, North China DOI Creative Commons

Shanming Wei,

Yaxin Zhang, Zizhao Cai

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 264, P. 120292 - 120292

Published: Nov. 7, 2024

Language: Английский

Citations

2
A Groundwater Quality Assessment Model for Water Quality Index: Combining Principal Component Analysis, Entropy Weight Method, and Coefficient of Variation Method for Dimensionality Reduction and Weight Optimization, and Its Application. DOI
Beibei Zhang,

Xin Hu,

Bo Li

et al.

Water Environment Research, Journal Year: 2024, Volume and Issue: 96(12)

Published: Dec. 1, 2024

Abstract Groundwater underpins water supply for most of the world's regions, yet its sustainable utilization has been markedly compromised by inappropriate exploitation and a multitude pollution sources. Water quality evaluation emerged as an essential strategy to guarantee optimized vigilant conservation resources. In this study, principal component analysis (PCA), entropy weight method (EWM), coefficient variation (CVM), Quality Index (WQI) were used construct integrated WQI groundwater assessment model that integrates PCA‐CVM‐EWM dimensionality reduction optimization. Taking village in Shandong Province, China, example, PCA identified seven indicators. The CVM‐EWM coupled calculate comprehensive weights through principle minimum information entropy, followed based on values. results indicated Class III predominated study area, accounting 74%, with localized present. hydrochemical type was primarily SO 4 ·HCO 3 ‐Ca, significantly influenced human activities. coefficients Fe, Mn, NH ‐N all exceeded 1. Compared other methods, demonstrated superior performance selection evaluative indicators, distribution, assessment, showing distinct advantage data numerous indicators substantial variation. findings provided scientific reference diagnosing issues formulating preventive control measures. Practitioner Points A index constructed. Optimized steps selecting assigning model. Selection indicator correlation analysis. variability is considered.

Language: Английский

Citations

2