El-Shafeiy Engy, Alsabaan Maazen, Ibrahem Mohamed I, Elwahsh Haitham
Department of Computer Science, Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City 32897, Monufia, Egypt.
Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel). 2023 Oct 20;23(20):8613. doi: 10.3390/s23208613.
With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies.
随着自动化系统、物联网（IoT）以及用于实时水质监测的传感器使用的增加，对意外值的及时检测有了更高的要求。技术故障可能会引入异常情况，并且大量的输入数据速率可能会使人工检测错误数据变得困难。本研究将一种开创性技术——带有长短期记忆的多元多卷积网络（MCN-LSTM）引入并应用于实时水质监测。MCN-LSTM是一种前沿的深度学习技术，旨在解决在复杂时间序列数据中检测异常的难题，特别是在实际环境中的水质监测。对自动化系统、物联网（IoT）和传感器网络用于持续水质监测的日益依赖，推动了MCN-LSTM方法的开发和部署。随着这些技术的更广泛应用，快速而精确地识别意外或异常数据点变得至关重要。技术难题、固有噪声以及高数据涌入对人工异常检测过程构成了重大障碍。MCN-LSTM技术通过整合多卷积网络和长短期记忆网络来利用深度学习。这种方法的结合在多元时间序列数据中提供了高效且有效的异常检测，能够识别和标记可能表明水质问题的意外模式或值。水质数据异常可能会产生深远影响，影响未来的分析并导致错误判断。异常识别必须精确，以避免不准确的结果并确保水质测试的完整性。利用从水质监测场景中安装的传感器获得的实际信息，进行了广泛的测试以验证MCN-LSTM技术。这些研究结果证明了MCN-LSTM的卓越功效，准确率高达92.3%。这种高精度水平证明了该技术能够实时区分正常和异常数据实例。MCN-LSTM技术在水质监测方面向前迈出了一大步。它可以改善决策过程，并减少未检测到的异常所导致的不良后果。在一个日益依赖自动化监测系统和物联网技术的时代，这种独特的技术通过促进供水的安全性和可持续性，在保护人类健康和维护环境方面具有重大前景。
