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<p>This work proposes a novel Internet of Nano-Things (IoNT)-driven real-time system architecture of water quality (WQ) observation and classification through a Convolutional Neural Network (CNN) framework, comprising WQI-CNN. The proposed system will be organized into four stages, namely, the data acquisition, coordination, data processing, and prediction and classification of the WQ Index (WQI). State-of-the-art nanosensors, such as Luminescent TOP, Surface Enhanced Raman Spectroscopy (SERS), and graphene-based sensors are used in the sensing phase to measure important WQ parameters. The data processing step uses Deep Generative Adversarial Networks (GANs) to fill in the gap between missing information and normalize data and improve the quality of predictions. WQI-CNN model incorporates these pre-processed inputs and uses CNN to create accurate WQI classification. The system was compared with the already existing systems such as the IoT-ML, WQI-ML, GTV-STP, which showed better performance in terms of computation time, RMSE, accuracy and MCC. The WQI-CNN model can be accurately used to determine the value of a real-time WQ monitor (98.91) which is essential in the management of the proactive water under the condition of the set of the safe drinking water standards.</p>

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Accurate water quality assessment using IoNT-enabled deep learning frameworks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

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  • V. Rajakumareswaran,
  • K. V. Uma,
  • Sheshagiri Babu,
  • N. Rajkumar

摘要

This work proposes a novel Internet of Nano-Things (IoNT)-driven real-time system architecture of water quality (WQ) observation and classification through a Convolutional Neural Network (CNN) framework, comprising WQI-CNN. The proposed system will be organized into four stages, namely, the data acquisition, coordination, data processing, and prediction and classification of the WQ Index (WQI). State-of-the-art nanosensors, such as Luminescent TOP, Surface Enhanced Raman Spectroscopy (SERS), and graphene-based sensors are used in the sensing phase to measure important WQ parameters. The data processing step uses Deep Generative Adversarial Networks (GANs) to fill in the gap between missing information and normalize data and improve the quality of predictions. WQI-CNN model incorporates these pre-processed inputs and uses CNN to create accurate WQI classification. The system was compared with the already existing systems such as the IoT-ML, WQI-ML, GTV-STP, which showed better performance in terms of computation time, RMSE, accuracy and MCC. The WQI-CNN model can be accurately used to determine the value of a real-time WQ monitor (98.91) which is essential in the management of the proactive water under the condition of the set of the safe drinking water standards.