article · IEEE Sensors Journal
On-site water quality monitoring requires near real-time data processing to identify changes quickly, protecting public health, biodiversity, and agriculture. A Highway-Bidirectional Long Short-term Memory classification tool has been designed for potential deployment in edge-enabled monitoring systems to conduct on-site evaluation. When evaluated against established baseline models, the classifier achieved superior performance across accuracy, precision, sensitivity, and F1-score metrics. It improved accuracy, precision, sensitivity, and F1-score by two percent over random forest, four percent over gradient boosting, and one percent over artificial neural networks. Compared to support vector machines, it showed a four percent improvement in accuracy, sensitivity, and F1-score, alongside a three percent gain in precision. The model also demonstrated rare errors when categorising complex water quality samples, providing a reliable basis for environmental management.
Prompt detection of shifts in water condition helps protect community health, conserve biodiversity, and prevent severe agricultural disruptions. Deploying accurate classification algorithms directly onto edge devices allows water data to be evaluated on site in near real-time. This reduces delays associated with off-site testing, enabling faster and more dependable environmental decision-making when water contamination or quality degradation occurs.
The tool is designed for integration into edge-enabled monitoring systems for on-site water testing, potentially serving environmental managers, agricultural operators, and public health bodies. Judged strictly from the reported comparative evaluation against standard machine learning baselines, the technology represents applied and tested algorithmic research that would require embedded hardware integration and field testing before reaching real-world deployment.
AI-generated from the published abstract. Always read the original work before citing.
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 4% increase in accuracy, sensitivity, F1-score, and 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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1109/jsen.2024.3383887
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
