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

• Published in: Water (Basel) Vol. 14; no. 18; p. 2801
• Main Authors: Derdour, Abdessamed, Jodar-Abellan, Antonio, Pardo, Miguel Ángel, Ghoneim, Sherif S. M., Hussein, Enas E.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Algorithms; Aquifers; Case studies; Classification; Data collection; Developing countries; Discriminant analysis; Drinking water; drinking water quality; Groundwater; Groundwater availability; Groundwater management; Laboratories; LDCs; Learning algorithms; Machine learning; Municipalities; Nitrates; Population growth; prediction model; Prediction models; Quality control; regional management; support decision-plans; Support vector machines; Sustainability; Sustainable development; Water analysis; Water quality; Water quality control; Water resources; Water sampling; Water scarcity; Algeria; India
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14182801

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.