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Modelling and Prediction of Water Quality by Using Artificial Intelligence

Author

Listed:
  • Mosleh Hmoud Al-Adhaileh

    (Deanship of E-Learning and Distance Education King Faisal University Saudi Arabia, Al-Ahsa P.O. Box 4000, Saudi Arabia)

  • Fawaz Waselallah Alsaade

    (College of Computer Science and Information Technology, King Faisal University, Al-Ahsa P.O. Box 4000, Saudi Arabia)

Abstract

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.

Suggested Citation

  • Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4259-:d:534381
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    References listed on IDEAS

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    1. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    2. Tawfiq Al-Mughanam & Theyazn H. H. Aldhyani & Belal Alsubari & Mohammed Al-Yaari, 2020. "Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
    3. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
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    5. Seoro Lee & Jonggun Kim & Gwanjae Lee & Jiyeong Hong & Joo Hyun Bae & Kyoung Jae Lim, 2021. "Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    6. Wahid Ali Hamood Altowayti & Shafinaz Shahir & Taiseer Abdalla Elfadil Eisa & Maged Nasser & Muhammad Imran Babar & Abdullah Faisal Alshalif & Faris Ali Hamood AL-Towayti, 2022. "Smart Modelling of a Sustainable Biological Wastewater Treatment Technologies: A Critical Review," Sustainability, MDPI, vol. 14(22), pages 1-32, November.
    7. André Felipe Henriques Librantz & Fábio Cosme Rodrigues dos Santos, 2023. "Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    8. Yas Barzegar & Irina Gorelova & Francesco Bellini & Fabrizio D’Ascenzo, 2023. "Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)," IJERPH, MDPI, vol. 20(15), pages 1-20, August.

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