文献类型：期刊文献

英文题名：ISSA optimized spatiotemporal prediction model of dissolved oxygen for marine ranching integrating DAM and Bi-GRU

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, Guangdong, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Smart Ocean Sensor Network & Equipm, Zhanjiang, Guangdong, Peoples R China

年份：2024

卷号：11

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001345162700001)、、Scopus(收录号：2-s2.0-85208546228)、WOS

基金：The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Key R&D Program of Shaanxi Province (2023-ZDLGY-15); General Project of National Natural Science Foundation of China (51979045); 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); University level doctoral initiation project (060302112309); Guangdong Youth Fund Project (2023A15151110770); Zhanjiang Marine Youth Talent Innovation Project (2023E0010).

语种：英文

外文关键词：marine ranching; dissolved oxygen prediction; improved sparrow search algorithm (ISSA); dual attention mechanism; Bi-GRU

外文摘要：In marine ranching aquaculture, dissolved oxygen (DO) is a crucial parameter that directly impacts the survival, growth, and profitability of cultured organisms. To effectively guide the early warning and regulation of DO in aquaculture waters, this study proposes a hybrid model for spatiotemporal DO prediction named PCA-ISSA-DAM-Bi-GRU. Firstly, principal component analysis (PCA) is applied to reduce the dimensionality of the input data and eliminate data redundancy. Secondly, an improved sparrow search algorithm (ISSA) based on multi strategy fusion is proposed to enhance the optimization ability and convergence speed of the standard SSA by optimizing the population initialization method, improving the location update strategies for discoverers and followers, and introducing a Cauchy-Gaussian mutation strategy. Thirdly, a feature and temporal dual attention mechanism (DAM) is incorporated to the baseline temporal prediction model Bi-GRU to construct a feature extraction network DAM-Bi-GRU. Fourthly, the ISSA is utilized to optimize the hyperparameters of DAM-Bi-GRU. Finally, the proposed model is trained, validated, and tested using water quality and meteorological parameter data collected from a self-built LoRa+5G-based marine ranching aquaculture monitoring system. The results show that: (1) Compared with the baseline model Bi-GRU, the addition of PCA, ISSA and DAM module can effectively improve the prediction performance of the model, and their fusion is effective; (2) ISSA demonstrates superior capability in optimizing model hyperparameters and convergence speed compared to traditional methods such as standard SSA, genetic algorithm (GA), and particle swarm optimization (PSO); (3) The proposed hybrid model achieves a root mean square error (RMSE) of 0.2136, a mean absolute percentage error (MAPE) of 0.0232, and a Nash efficient (NSE) of 0.9427 for DO prediction, outperforming other similar data-driven models such as IBAS-LSTM and IDA-GRU. The prediction performance of the model meets the practical needs of precise DO prediction in aquaculture.