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Article

A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture

State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, School of Computer Science and Cyberspace Security, Hainan University, Haikou 570228, China
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Academic Editors: Long Wang, Chao Huang and Zhenhua Wang
Water 2021, 13(20), 2907; https://doi.org/10.3390/w13202907
Received: 15 August 2021 / Revised: 11 October 2021 / Accepted: 12 October 2021 / Published: 16 October 2021
(This article belongs to the Special Issue New Perspectives in Agricultural Water Management)
In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively. View Full-Text
Keywords: aquaculture water quality prediction; TCN deep learning; smart mariculture aquaculture water quality prediction; TCN deep learning; smart mariculture
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MDPI and ACS Style

Fu, Y.; Hu, Z.; Zhao, Y.; Huang, M. A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture. Water 2021, 13, 2907. https://doi.org/10.3390/w13202907

AMA Style

Fu Y, Hu Z, Zhao Y, Huang M. A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture. Water. 2021; 13(20):2907. https://doi.org/10.3390/w13202907

Chicago/Turabian Style

Fu, Yuexin, Zhuhua Hu, Yaochi Zhao, and Mengxing Huang. 2021. "A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture" Water 13, no. 20: 2907. https://doi.org/10.3390/w13202907

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