<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using Deep Learning

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This work is licensed under the Creative Commons Attribution 4.0 International License.

Aquaculture plays a significant role in both economic development and food production. Maintaining an ecological environment with good water quality is...

Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using Deep Learning

Online veröffentlicht: 28. Juni 2023

Seitenbereich: 1 - 16

DOI: https://doi.org/10.2478/seeur-2023-0030

Schlüsselwörter
Anomaly Detection, Deep Learning, Aquaculture, LSTM-AE, Autoencoder, Variational Autoencoder, SR-CNN

© 2023 Aleksandar Petkovski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using Deep Learning

Online veröffentlicht: 28. Juni 2023

Seitenbereich: 1 - 16

DOI: https://doi.org/10.2478/seeur-2023-0030

SchlüsselwörterAnomaly Detection, Deep Learning, Aquaculture, LSTM-AE, Autoencoder, Variational Autoencoder, SR-CNN

© 2023 Aleksandar Petkovski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Anomaly Detection, Deep Learning, Aquaculture, LSTM-AE, Autoencoder, Variational Autoencoder, SR-CNN