基于深度学习的智能水产养殖单变量感知时间序列数据异常检测

Aleksandar Petkovski, Visar Shehu
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引用次数: 0

摘要

水产养殖在经济发展和粮食生产中发挥着重要作用。维持良好水质的生态环境是保证水产养殖生产效率和质量的关键。有效的水质管理可以防止异常情况,并对粮食安全作出重大贡献。检测水产养殖环境中的异常情况对于确保正确维持环境以满足健康和适当的养鱼要求至关重要。本文的重点是使用深度学习技术来检测水产养殖环境中水质数据的异常。利用从物联网水产养殖系统中收集的多个真实传感器数据集,分析了四种深度学习异常检测技术,包括自编码器、变分自编码器、长短期记忆自编码器和频谱残差卷积神经网络。对温度、溶解氧和pH参数进行了大量的实验,评价分析表明,长短期记忆自编码器异常检测方法在温度和氧数据集上的异常检测效果很好,而光谱残差卷积神经网络在pH数据集上的检测效果最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using Deep Learning
Abstract Aquaculture plays a significant role in both economic development and food production. Maintaining an ecological environment with good water quality is essential to ensure the production efficiency and quality of aquaculture. Effective management of water quality can prevent abnormal conditions and contribute significantly to food security. Detecting anomalies in the aquaculture environment is crucial to ensure that the environment is maintained correctly to meet healthy and proper requirements for fish farming. This article focuses on the use of deep learning techniques to detect anomalies in water quality data in the aquaculture environment. Four deep learning anomaly detection techniques, including Autoencoder, Variational Autoencoder, Long-Short Term Memory Autoencoder, and Spectral-Residual Convolutional Neural Network, were analysed using multiple real-world sensor datasets collected from IoT aquaculture systems. Extensive experiments were conducted for temperature, dissolved oxygen, and pH parameters, and the evaluation analysis revealed that the Long-Short Term Memory Autoencoder anomaly detection method showed promising results in detecting anomalies for the temperature and oxygen datasets, while the Spectral-Residual Convolutional Neural Network demonstrated the best performance on the pH datasets.
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