Abstract
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.
Highlights
In traditional mariculture, farmers can only rely on breeding experience to control the water quality, and it is impossible to immediately grasp a drop in aquatic production caused by water quality problems
Aiming at the long-term prediction of key water quality parameters in smart mariculture, this paper proposes a prediction method based on temporal convolutional network
After preprocessing the data with linear filling and sliding filtering, we use the Pearson correlation coefficient to analyze the correlation of water quality parameters
Summary
Farmers can only rely on breeding experience to control the water quality, and it is impossible to immediately grasp a drop in aquatic production caused by water quality problems. The prediction of water quality can help farmers foresee future changes in water quality parameters and promptly remind farmers to take measures that make sure that the fish are living and growing in the most suitable environment It can improve the quality of aquatic products while increasing the yield [1,2,3]. Liu et al [15,16] used the SRU (simple recirculation unit) network and improved deep Bi-SRU (bi-directional stacked simple recirculation unit) network to predict multiple water quality parameters These methods still have problems, such as a complex network structure and high time complexity, which increase the hardware requirements. Because of a large number of data samples in this paper, we use the Pearson correlation coefficient method [18] to analyze the direct correlation of different water quality factors
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