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

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  • 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
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    References listed on IDEAS

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    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.
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    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
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    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.

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