首页|DA-Bi-SRU for water quality prediction in smart mariculture
DA-Bi-SRU for water quality prediction in smart mariculture
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2022 Elsevier B.V.Due to the open nature of the mariculture environment, water quality factors are susceptible to the cross-influence of biology, physics, chemistry, hydrometeorology and human production activities. The changes of water quality parameters have the characteristics of non-linearity, dynamics, variability and complexity. We propose a novel water quality prediction model for pH, water temperature and dissolved oxygen, namely Double-Attention-Based Bidirectional Simple Recurrent Unit model (DA-Bi-SRU). First, we construct a new huge original dataset collected in time series, consisting of 23,000 sets of data. Then, the collected water quality parameters are sequentially preprocessed. Finally, we introduce a dual attention mechanism module for feature extraction and temporal sequences in the Bi-SRU model. Using the correlations between the water quality parameters and temporal dependencies information, the proposed model can significantly improve the accuracy of long-term prediction of water quality. The experimental results show that our DA-Bi-SRU model has higher prediction accuracy than the methods based on RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory) and Bi-SRU, and the prediction accuracy can reach 93.06%. Therefore, in smart mariculture, farmers can know the changing trend of water quality in advance through our proposed method, and take timely countermeasures before the deterioration of aquaculture ecology.
NSTL
Elsevier
