IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v39y2025i8d10.1007_s11269-025-04122-9.html
   My bibliography  Save this article

A New Integrated Water Quality Evaluation Approach Combined with Different Evaluation Methods Based on Weighted Membership Degree

Author

Listed:
  • Won-Chol Yang

    (Kim Chaek University of Technology)

  • Jae-Bok Ri

    (Kim Chaek University of Technology)

  • Myong-Song Om

    (Kim Chaek University of Technology)

  • Jin-Sim Kim

    (Kim Chaek University of Technology)

  • Un-Ha Kim

    (Kim Chaek University of Technology)

  • Wi-Song Ri

    (Kim Chaek University of Technology)

  • Sun-Hak Sok

    (Kim Chaek University of Technology)

Abstract

Water quality evaluation (WQE) is very important for environment protection and management. There are many WQE methods, and the results from the different methods may differ with one another. However, it is unknown which is more reasonable method. The purpose of this paper is to develop a reasonable approach to determine the final WQ grades/ranks (WQGs) by integrating the WQGs from the individual methods. We develop a novel integrated WQE approach combined with multiple WQE methods based on weighted membership degree. It consists of the following steps: determining the WQGs of the evaluation objects using multiple WQE methods, determining priority weights of the individual methods, calculating weighted membership degrees that the object are evaluated to every grades/ranks, and calculating final WQG indices of the objects. It was applied to two application cases. The first case evaluated the final eutrophication grades of 12 lakes by integrating the grades from scoring index method, variable fuzzy sets method, hybrid fuzzy and optimal model, and neural network method. The second case evaluated the final WQ ranks of 29 wells by integrating the ranks from WQI method, compromise programing method with p = 1 and p = 2, ordered weighted averaging method, and TOPSIS method. In two cases, the proposed approach had the maximum correlation and minimum deviation from the other methods. It illustrated the reasonability of the proposed approach. It could be also used to integrate the environmental quality evaluation results obtained from various evaluation methods in many practical environmental quality evaluation problems.

Suggested Citation

  • Won-Chol Yang & Jae-Bok Ri & Myong-Song Om & Jin-Sim Kim & Un-Ha Kim & Wi-Song Ri & Sun-Hak Sok, 2025. "A New Integrated Water Quality Evaluation Approach Combined with Different Evaluation Methods Based on Weighted Membership Degree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(8), pages 3679-3696, June.
  • Handle: RePEc:spr:waterr:v:39:y:2025:i:8:d:10.1007_s11269-025-04122-9
    DOI: 10.1007/s11269-025-04122-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-025-04122-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-025-04122-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.
    2. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    3. Ivana I. Mladenović-Ranisavljević & Lj. Takić & Đ. Nikolić, 2018. "Water Quality Assessment Based on Combined Multi-Criteria Decision-Making Method with Index Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2261-2276, May.
    4. Marwa El-Shebli & Yousef Sharrab & Dimah Al-Fraihat, 2024. "Correction: Prediction and modeling of water quality using deep neural networks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(6), pages 16317-16317, June.
    5. José Roberto Ribas & Juliana Crenitte Ribas Severo & Letícia Correa Gonçalves, 2024. "A Fuzzy Multicriteria Approach to Estimate the Water Quality Index of Hydroelectric Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 2155-2170, April.
    6. M. A. Bonora & G. Capano & A. Rango & Mario Maiolo, 2022. "Novel Eulerian Approach with Cellular Automata Modelling to Estimate Water Quality in a Drinking Water Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5961-5976, December.
    7. Feng Yan & Ling Liu & You Zhang & Musong Chen & Ning Chen, 2016. "The Research of Dynamic Variable Fuzzy Set Assessment Model in Water Quality Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 63-78, January.
    8. Marwa El-Shebli & Yousef Sharrab & Dimah Al-Fraihat, 2024. "Prediction and modeling of water quality using deep neural networks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 11397-11430, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nashwa A. Shaaban & David K. Stevens, 2025. "Transforming Complex Water Quality Monitoring Data into Water Quality Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(8), pages 3883-3899, June.
    2. Sayiter Yıldız & Can Bülent Karakuş, 2020. "Estimation of irrigation water quality index with development of an optimum model: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4771-4786, June.
    3. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    4. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    5. Mohammad Ali Baghapour & Mohammad Reza Shooshtarian & Mahdi Zarghami, 2020. "Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1155-1172, February.
    6. Ranković, Vesna & Radulović, Jasna & Radojević, Ivana & Ostojić, Aleksandar & Čomić, Ljiljana, 2010. "Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia," Ecological Modelling, Elsevier, vol. 221(8), pages 1239-1244.
    7. Junguo, Hu & Guomo, Zhou & Xiaojun, Xu, 2013. "Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data," Ecological Modelling, Elsevier, vol. 266(C), pages 86-96.
    8. Shanshan Wang & Joe Wiart, 2020. "Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks," IJERPH, MDPI, vol. 17(9), pages 1-15, April.
    9. Ran Li & Yanqiang Gao & Yihong Guan & Mou Lv & Hang Li, 2025. "Optimization and Reliability Analysis of the Combined Application of Multiple Air Tanks Under Extreme Accident Conditions Based on the Multi-Objective Whale Optimization Algorithm," Sustainability, MDPI, vol. 17(5), pages 1-23, March.
    10. West, David & Dellana, Scott, 2011. "An empirical analysis of neural network memory structures for basin water quality forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 777-803, July.
    11. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    12. Tanujit Chakraborty & Ashis Kumar Chakraborty & Zubia Mansoor, 2019. "A hybrid regression model for water quality prediction," OPSEARCH, Springer;Operational Research Society of India, vol. 56(4), pages 1167-1178, December.
    13. Tat Pham Van & Pham Nu Ngoc Han & Minh Phap Dao, 2017. "Modelling of Dissolved Oxygen in Thi Vai River Water Incorporating Artificial Neural Network and Multivariable Regression," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 7(1), pages 11-18, November.
    14. Zoran Štirbanović & Vojka Gardić & Dragiša Stanujkić & Radmila Marković & Jovica Sokolović & Zoran Stevanović, 2021. "Comparative MCDM Analysis for AMD Treatment Method Selection," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3737-3753, September.
    15. S. Vijay & K. Kamaraj, 2021. "Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 535-553, January.
    16. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    17. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.
    18. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    19. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
    20. Yi Liang & Haichao Wang, 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road," Energies, MDPI, vol. 14(12), pages 1-19, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:39:y:2025:i:8:d:10.1007_s11269-025-04122-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.