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Improving Water Quality Index Prediction Using Regression Learning Models

Author

Listed:
  • Jesmeen Mohd Zebaral Hoque

    (Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia)

  • Nor Azlina Ab. Aziz

    (Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia)

  • Salem Alelyani

    (Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
    College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia)

  • Mohamed Mohana

    (Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia)

  • Maruf Hosain

    (Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia)

Abstract

Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13702-:d:949916
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    References listed on IDEAS

    as
    1. 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.
    2. Huan Wu & Shuiping Cheng & Kunlun Xin & Nian Ma & Jie Chen & Liang Tao & Min Gao, 2022. "Water Quality Prediction Based on Multi-Task Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, August.
    3. Basilua Andre Muzembo & Kei Kitahara & Anusuya Debnath & Ayumu Ohno & Keinosuke Okamoto & Shin-Ichi Miyoshi, 2022. "Cholera Outbreaks in India, 2011–2020: A Systematic Review," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    4. Bijoyee Sarker & Kamrun N. Keya & Fatin I. Mahir & Khandakar M. Nahiun & Shahirin Shahida & Ruhul A. Khan, 2021. "Surface and Ground Water Pollution: Causes and Effects of Urbanization and Industrialization in South Asia," Scientific Review, Academic Research Publishing Group, vol. 7(3), pages 32-41, 07-2021.
    5. Manickavasagar Kayanan & Pushpakanthie Wijekoon, 2020. "Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model," Journal of Probability and Statistics, Hindawi, vol. 2020, pages 1-7, March.
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