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The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa

Author

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  • Koketso J. Setshedi

    (Environmental Health and Biotechnology Research Group, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Rhodes University, Makhanda 6139, South Africa)

  • Nhamo Mutingwende

    (Environmental Health and Biotechnology Research Group, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Rhodes University, Makhanda 6139, South Africa)

  • Nosiphiwe P. Ngqwala

    (Environmental Health and Biotechnology Research Group, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Rhodes University, Makhanda 6139, South Africa)

Abstract

Reliable prediction of water quality changes is a prerequisite for early water pollution control and is vital in environmental monitoring, ecosystem sustainability, and human health. This study uses Artificial Neural Network (ANN) technique to develop the best model fits to predict water quality parameters by employing multilayer perceptron (MLP) neural network and the radial basis function (RBF) neural network, using data collected from three district municipalities. Two input combination models, MLP-4-5-4 and MLP-4-9-4, were trained, verified, and tested for their predictive performance ability, and their physicochemical prediction accuracy was compared by using each model’s observed data with the predicted data. The MLP-4-5-4 model showed a better understanding of the data sets and water quality predictive ability giving an MSE of 39.06589 and a correlation coefficient (R 2 ) of the observed and the predicted water quality of 0.989383 compared to the MLP-4-9-4 model (R 2 = 0.993532, MSE = 39.03087). These results apply to natural water resources management in South Africa and similar catchment systems. The MLP-4-5-4 system can be scaled up for future water quality prediction of the Waste Water Treatment Plants (WWTPs), groundwater, and surface water while raising awareness among the public and industry on future water quality.

Suggested Citation

  • Koketso J. Setshedi & Nhamo Mutingwende & Nosiphiwe P. Ngqwala, 2021. "The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa," IJERPH, MDPI, vol. 18(10), pages 1-17, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5248-:d:554947
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    References listed on IDEAS

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    1. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
    2. Yashon O. Ouma & Clinton O. Okuku & Evalyne N. Njau, 2020. "Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya," Complexity, Hindawi, vol. 2020, pages 1-23, May.
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    Cited by:

    1. Jesmeen Mohd Zebaral Hoque & Nor Azlina Ab. Aziz & Salem Alelyani & Mohamed Mohana & Maruf Hosain, 2022. "Improving Water Quality Index Prediction Using Regression Learning Models," IJERPH, MDPI, vol. 19(20), pages 1-23, October.

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