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Article

Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

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Laboratory for the Sustainable Management of Natural Resources in Arid and Semi-Arid Zones, University Center Salhi Ahmed Naama (Ctr Univ Naama), P.O. Box 66, Naama 45000, Algeria
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Soil and Water Conservation Group, Spanish Research Council, Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC), P.O. Box 164, 30100 Murcia, Spain
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University Institute of Water and Environmental Sciences, University of Alicante, 03690 Alicante, Spain
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Department of Civil Engineering, University of Alicante, 03690 Alicante, Spain
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Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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National Water Research Center, Shubra El-Kheima 13411, Egypt
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Authors to whom correspondence should be addressed.
Academic Editor: Roohollah Noori
Water 2022, 14(18), 2801; https://doi.org/10.3390/w14182801
Received: 30 July 2022 / Revised: 23 August 2022 / Accepted: 6 September 2022 / Published: 9 September 2022
Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans. View Full-Text
Keywords: prediction model; regional management; drinking water quality; support decision-plans prediction model; regional management; drinking water quality; support decision-plans
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MDPI and ACS Style

Derdour, A.; Jodar-Abellan, A.; Pardo, M.Á.; Ghoneim, S.S.M.; Hussein, E.E. Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms. Water 2022, 14, 2801. https://doi.org/10.3390/w14182801

AMA Style

Derdour A, Jodar-Abellan A, Pardo MÁ, Ghoneim SSM, Hussein EE. Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms. Water. 2022; 14(18):2801. https://doi.org/10.3390/w14182801

Chicago/Turabian Style

Derdour, Abdessamed, Antonio Jodar-Abellan, Miguel Ángel Pardo, Sherif S. M. Ghoneim, and Enas E. Hussein. 2022. "Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms" Water 14, no. 18: 2801. https://doi.org/10.3390/w14182801

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