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Prediction and modeling of water quality using deep neural networks

Author

Listed:
  • Marwa El-Shebli

    (Oklahoma State University)

  • Yousef Sharrab

    (Isra University)

  • Dimah Al-Fraihat

    (Isra University)

Abstract

Water pollution is one of the most challenging environmental issues. A powerful tool for measuring the suitability of water for drinking is required. The Water Quality Index (WQI) is a widely used parameter for the assessment of water quality through mathematical formulas. In this paper, a Deep Neural Network (DNN) model is developed to forecast WQI based on parameters selected for the dry and wet seasons throughout the year. Statistical modeling and unsupervised machine learning techniques are used. These modelings include the Principal Component Analysis/Factor Analysis (PCA/FA) which is used to interpret seasonal changes and the sources of springs under study. The other modeling technique utilized in this study is the Hierarchical Cluster Analysis (HCA). The results of this study reveal that the developed DNN model has achieved a high accuracy of ***. The goodness of fit of the developed model using R-Squared (R2) is 0.98 which is deemed high. The Mean Square Error metric is close to zero. Furthermore, the PCA/FA revealed five major parameters that impact water quality which together account for 92% of the total variance of water quality in summer and 96% in winter. Moreover, results show that the average of the WQI for all springs is of poor water quality at 46.75% during the dry season and medium water quality at 55.5% during the wet season.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:5:d:10.1007_s10668-023-03335-5
    DOI: 10.1007/s10668-023-03335-5
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    References listed on IDEAS

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    3. Margaret W. Gitau & Jingqiu Chen & Zhao Ma, 2016. "Water Quality Indices as Tools for Decision Making and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2591-2610, June.
    4. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
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