Analysis of water quality indices and machine learning techniques for rating water pollution: a case study of Rawal Dam, Pakistan

• Published in: Water science & technology. Water supply 2021-09, Vol.21 (6), p.3225-3250
• Main Authors: Ahmed, Mehreen, Mumtaz, Rafia, Hassan Zaidi, Syed Mohammad
• Format: Article
• Language: English
• Subjects: Algorithms; Bayesian analysis; Chloride; Classification; Coliforms; Data acquisition; Data sampling; decision tree; Decision trees; Dissolved oxygen; Geographical information systems; Hydrology; Inlets (waterways); Internet of Things; iot; Learning algorithms; Machine learning; Magnesium; Mathematical models; Multilayer perceptrons; Nitrates; Parameter sensitivity; Parameters; physico-chemical; Potassium; Principal components analysis; Quality assessment; Sampling; Streams; Temporal variations; Turbidity; Uncertainty; Water analysis; Water filtration; Water pollution; Water purification; Water quality; Water quality assessments; water quality index; water quality monitoring; Water sampling; Water types; Wind
• ISSN: 1606-9749; 1607-0798
• DOI: 10.2166/ws.2021.082

Water Quality Index (WQI) is a unique and effective rating technique for assessing the quality of water. Nevertheless, most of the indices are not applicable to all water types as these are dependent on core physico-chemical water parameters that can make them biased and sensitive towards specific attributes including: (i) time, location and frequency for data sampling; (ii) number, variety and weights allocation of parameters. Therefore, there is a need to evaluate these indices to eliminate uncertainties that make them unpredictable and which may lead to manipulation of the water quality classes. The present study calculated five WQIs for two temporal periods: (i) June to December 2019 obtained in real time (using the Internet of Things (IoT) nodes) at inlet and outlet streams of Rawal Dam; (ii) 2012–2019 obtained from the Rawal Dam Water Filtration Plant, collected through GIS-based grab sampling. The computed WQIs categorized the collected datasets as ‘Very Poor’, primarily owing to the uneven distribution of the water samples that has led to class imbalance in the data. Additionally, this study investigates the classification of water quality using machine learning algorithms namely: Decision Tree (DT), k-Nearest Neighbor (KNN), Logistic Regression (LogR), Multilayer Perceptron (MLP) and Naive Bayes (NB); based on the parameters including: pH, dissolved oxygen, conductivity, turbidity, fecal coliform and temperature. The classification results showed that the DT algorithm outperformed other models with a classification accuracy of 99%. Although WQI is a popular method used to assess the water quality, there is a need to address the uncertainties and biases introduced by the limitations of data acquisition (such as specific location/area, type and number of parameters or water type) leading to class imbalance. This can be achieved by developing a more refined index that considers various other factors such as topographical and hydrological parameters with spatial temporal variations combined machine learning techniques to effectively contribute in estimation of water quality for all regions.