River Water Quality Classification using a Hybrid Machine Learning Technique

Water is essential for the life and nourishment of all living things. River water quality has deteriorated in recent years as a result of toxic wastes and contaminants. This rapid augmentation in water pollution is a major source of concern since it degrades water quality and renders it unsuitable for any usage. In India, Yamuna is considered one of the most important rivers in the northern region which plays a major role in development. In this work, we have focused on the Yamuna River water quality classification according to its usability as excellent, poor and unsuitable for drinking. To achieve this objective, we have presented a novel hybrid machine learning technique by taking the advantage of an ensemble learning mechanism. The proposed approach considers relief-based feature selection which helps to reduce the dimensionality, forward feature ranking which helps to consider the stage-wise features and a decision tree classifier. The performance of proposed water quality classification technique is compared with other classification techniques including Naïve Bayes, Support Vector Machine (SVM), bagged tree and boosted tree classifiers. The proposed approach achieves an overall accuracy of 99.65% which proves the robustness of this scheme in terms of predicting the water quality.