Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices with Efficient Machine Learning Technique

Jingjing Xia and Jin Zeng ()
Additional contact information
Jingjing Xia: Wuhan University of Technology
Jin Zeng: Huazhong University of Science and Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 6, No 18, 2045-2060

Abstract: Abstract Water is an indispensable resource for human production and life. The evaluation of water quality by scientific methods provides sufficient support for the regeneration and recycling of water resources. In this study, entropy theory was used to evaluate water quality and overcomes the limitations of traditional water quality assessment, which does not consider the impact of different environmental factors on water quality. Considering the complexity of the traditional evaluation process, two typical machine learning (ML) methods – generalized regression neural network (GRNN) and support vector machine (SVM) – were used to predict the entropy water quality index (EWQI). Correlation analysis was applied to divide environmental factors into different combinations that subsequently acted as the input vector for the ML model. According to the results of the root mean squared error (RMSE), the SVM was selected as the better prediction model. Then, four different types of optimization algorithms were used to optimize the SVM to calculate nonlinear regression predictions and classifications of water quality. After analyzing the prediction results with different types of scientific evaluation indicators, the algorithm of differential evaluation and gray wolf optimization (DE-GWO) achieved markedly better performance than the other three algorithms, which has important advantages in avoiding the prediction model falling into a local optimal solution. The results of this study have significant guidance for water quality prediction and could make further contributions to the rational use and protection of water resources.

Keywords: Water quality; Correlation analysis; Machine learning method; Nonlinear regression prediction; Nonlinear regression classification (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-022-03126-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:36:y:2022:i:6:d:10.1007_s11269-022-03126-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-022-03126-z

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().