文献类型：期刊文献

英文题名：A Hybrid Improved Dual-Channel and Dual-Attention Mechanism Model for Water Quality Prediction in Nearshore Aquaculture

作者：Liu, Wenjing[1,2];Wang, Ji[1,2];Li, Zhenhua[1,2];Lu, Qingjie[1,2]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Smart Ocean Sensor Network & Equipm, Zhanjiang 524088, Peoples R China

年份：2025

卷号：14

期号：2

外文期刊名：ELECTRONICS

收录：SCI-EXPANDED(收录号：WOS:001405388300001)、、Scopus(收录号：2-s2.0-85215958313)、WOS

基金：This work was funded by the Key R&D Program of Shaanxi Province (2023-ZDLGY-15); Program for scientific research start-up funds of Guangdong Ocean University (060302112309); General Project of National Natural Science Foundation of China (62401162); New Generation Information Technology Special Project in Key Fields of Ordinary Universities in Guangdong Province (2020ZDZX3008); Key Special Project in the Field of Artificial Intelligence in Guangdong Province (2019KZDZX1046); Guangdong Youth Fund Project (2023A15151110770); Zhanjiang Marine Youth Talent Innovation Project (2023E0010).

语种：英文

外文关键词：nearshore aquaculture; water quality prediction; dual-channel; dual-attention mechanism

外文摘要：The aquatic environment in aquaculture serves as the foundation for the survival and growth of aquatic animals, while a high-quality water environment is a necessary condition for promoting efficient and healthy aquaculture development. To effectively guide early warnings and the regulation of water quality in aquaculture, this study proposes a predictive model based on a dual-channel and dual-attention mechanism, namely, the DAM-ResNet-LSTM model. This model encompasses two parallel feature extraction channels: a residual network (ResNet) and long short-term memory (LSTM), with dual-attention mechanisms integrated into each channel to enhance the model's feature representation capabilities. Then, the proposed model is trained, validated, and tested using water quality and meteorological parameter data collected by an offshore farm environmental monitoring system. The results demonstrate that the proposed dual-channel structure and dual-attention mechanism can significantly improve the predictive performance of the model. The prediction accuracy for pH, dissolved oxygen (DO), and salinity (SAL) (with Nash coefficients of 0.9361, 0.9396, and 0.9342, respectively) is higher than that for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), nitrite (NO2-), and active phosphate (AP) (with Nash coefficients of 0.8578, 0.8542, 0.8372, and 0.8294, respectively). Compared to the single-channel model DA-ResNet (ResNet integrated with the proposed dual-attention mechanism), the Nash coefficients for predicting pH, DO, SAL, COD, NH3-N, NO2-, and AP increase by 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, and 14.99%, respectively. Compared to the single-channel DA-LSTM model (LSTM integrated with the proposed dual-attention mechanism), the corresponding increases in Nash coefficients are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, and 10.2%, respectively. Compared to the ResNet-LSTM (ResNet and LSTM in parallel) model without the attention mechanism, the improvements in Nash coefficients are 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, and 4.13%, respectively. The predictive performance of the model fulfills the practical requirements for accurate forecasting of water quality in nearshore aquaculture.