IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5656-d810706.html
   My bibliography  Save this article

A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models

Author

Listed:
  • Roman Trach

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland)

  • Yuliia Trach

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland)

  • Agnieszka Kiersnowska

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland)

  • Anna Markiewicz

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland)

  • Marzena Lendo-Siwicka

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland)

  • Konstantin Rusakov

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland)

Abstract

Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer ( MAPE = 9.6%; R 2 = 0.964). A comparison of the MAPE and R 2 indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively.

Suggested Citation

  • Roman Trach & Yuliia Trach & Agnieszka Kiersnowska & Anna Markiewicz & Marzena Lendo-Siwicka & Konstantin Rusakov, 2022. "A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models," Sustainability, MDPI, vol. 14(9), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5656-:d:810706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5656/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/5656/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuliia Trach & Roman Trach & Marek Kalenik & Eugeniusz Koda & Anna Podlasek, 2021. "A Study of Dispersed, Thermally Activated Limestone from Ukraine for the Safe Liming of Water Using ANN Models," Energies, MDPI, vol. 14(24), pages 1-14, December.
    2. Abdulaziz Alqahtani & Muhammad Izhar Shah & Ali Aldrees & Muhammad Faisal Javed, 2022. "Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality," Sustainability, MDPI, vol. 14(3), pages 1-19, January.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    4. Richa Pandey & L.N. Pattanaik, 2014. "A fuzzy QFD approach to implement reverse engineering in prosthetic socket development," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 17(1), pages 1-14.
    5. Wojciech Drozd & Agnieszka Leśniak, 2018. "Ecological Wall Systems as an Element of Sustainable Development—Cost Issues," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    6. Muhammad Izhar Shah & Wesam Salah Alaloul & Abdulaziz Alqahtani & Ali Aldrees & Muhammad Ali Musarat & Muhammad Faisal Javed, 2021. "Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    7. Monika Kulisz & Justyna Kujawska & Bartosz Przysucha & Wojciech Cel, 2021. "Forecasting Water Quality Index in Groundwater Using Artificial Neural Network," Energies, MDPI, vol. 14(18), pages 1-17, September.
    8. Roman Trach & Yuliia Trach & Marzena Lendo-Siwicka, 2021. "Using ANN to Predict the Impact of Communication Factors on the Rework Cost in Construction Projects," Energies, MDPI, vol. 14(14), pages 1-15, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roman Trach & Oleksandr Khomenko & Yuliia Trach & Oleksii Kulikov & Maksym Druzhynin & Nataliia Kishchak & Galyna Ryzhakova & Hanna Petrenko & Dmytro Prykhodko & Olha Obodіanska, 2023. "Application of Fuzzy Logic and SNA Tools to Assessment of Communication Quality between Construction Project Participants," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    2. Roman Trach & Galyna Ryzhakova & Yuliia Trach & Andrii Shpakov & Volodymyr Tyvoniuk, 2023. "Modeling the Cause-and-Effect Relationships between the Causes of Damage and External Indicators of RC Elements Using ML Tools," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    3. Roman Trach & Victor Moshynskyi & Denys Chernyshev & Oleksandr Borysyuk & Yuliia Trach & Pavlo Striletskyi & Volodymyr Tyvoniuk, 2022. "Modeling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN," Sustainability, MDPI, vol. 14(23), pages 1-19, November.

    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. Roman Trach & Galyna Ryzhakova & Yuliia Trach & Andrii Shpakov & Volodymyr Tyvoniuk, 2023. "Modeling the Cause-and-Effect Relationships between the Causes of Damage and External Indicators of RC Elements Using ML Tools," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    2. Roman Trach & Oleksandr Khomenko & Yuliia Trach & Oleksii Kulikov & Maksym Druzhynin & Nataliia Kishchak & Galyna Ryzhakova & Hanna Petrenko & Dmytro Prykhodko & Olha Obodіanska, 2023. "Application of Fuzzy Logic and SNA Tools to Assessment of Communication Quality between Construction Project Participants," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    3. Yuliia Trach & Roman Trach & Marek Kalenik & Eugeniusz Koda & Anna Podlasek, 2021. "A Study of Dispersed, Thermally Activated Limestone from Ukraine for the Safe Liming of Water Using ANN Models," Energies, MDPI, vol. 14(24), pages 1-14, December.
    4. Roman Trach & Victor Moshynskyi & Denys Chernyshev & Oleksandr Borysyuk & Yuliia Trach & Pavlo Striletskyi & Volodymyr Tyvoniuk, 2022. "Modeling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    5. Yuliia Trach & Victor Melnychuk & Oleksandr Stadnyk & Roman Trach & Filip Bujakowski & Agnieszka Kiersnowska & Gabriela Rutkowska & Leonid Skakun & Jacek Szer & Eugeniusz Koda, 2023. "The Possibility of Implementation of West Ukrainian Paleogene Glauconite–Quartz Sands in the Building Industry: A Case Study," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    6. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    7. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    8. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    9. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    10. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
    11. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    12. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    13. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    14. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    15. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    16. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
    17. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    18. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    19. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    20. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.

    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:gam:jsusta:v:14:y:2022:i:9:p:5656-:d:810706. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.