Reliable water quality monitoring requires on-site processing and assessment of water quality data in near real-time. This helps to promptly detect changes in water quality, prevent biodiversity loss, safeguard the health and well-being of communities, and mitigate agricultural problems. To this end, we proposed a highway-bidirectional long short-term memory (Highway-BiLSTM)-based water quality classification tool for potential integration into an edge-enabled water quality monitoring system to facilitate on-site water quality classification. The performance of the proposed classifier was validated by comparing it with several baseline water quality classifiers. The proposed classifier outperformed the baseline water classifier in terms of accuracy, precision, sensitivity, F1 -score, and confusion matrix. Specifically, the proposed water classifier surpassed the random forest (RF) classifier with 2% accuracy, precision, sensitivity, and F1 -score. Moreover, the proposed classifier achieved an increase of 4% in accuracy, precision, sensitivity, and F1 -score for classifying water quality compared with the gradient-boosting classifier. Additionally, the proposed method has a 4% increase in accuracy, sensitivity, F1 -score, and a 3% increase in precision compared to the support vector machine (SVM) water quality classifier. The proposed method outperformed the artificial neural network (ANN) classifier by 1% accuracy, precision, sensitivity, and F1 -score. Finally, the proposed method demonstrated rare errors in accurately classifying complex water quality samples. These findings suggest that our proposed method could be used to effectively classify water quality to aid accurate decision-making and environmental management.
