Dissolved oxygen (DO) content is a significant indicator of water quality in intensive aquaculture ponds and is profoundly related to healthy fish growth. Accurately forecasting DO and analysing its change trends in aquaculture ponds is crucial for fish survival. A novel forecasting model based on a combination of complete ensemble empirical mode decomposition with an adaptive noise Lempel-Ziv complex (CEEMDAN-LZC) and a gated recurrent unit (GRU) with a generalized opposition-based learning particle swarm optimization algorithm (GOBLPSO) has been proposed to improve the prediction precision of DO. The DO content numerical sequence was decomposed and reconstructed into several new features by the CEEMDAN-LZC method in the modelling process. Independent models were structured to fit the components obtained above using GRUs, and the prediction value was superimposed to obtain the ultimate result. Furthermore, the time-step parameter t and the unit number parameter u, which significantly influence the performance of the GRU, were selected by the GOBLPSO algorithm. The simulation results based on DO content data in river crab culture ponds show that the proposed model has excellent essential feature extraction capability and is very powerful and reliable for DO content prediction in aquaculture.

溶解氧 (DO) 含量是集约化水产养殖池塘水质的重要指标，与鱼类的健康生长密切相关。准确预测 DO 并分析其在水产养殖池塘中的变化趋势对于鱼类的生存至关重要。基于完全集成经验模式分解与自适应噪声 Lempel-Ziv 复合体 (CEEMDAN-LZC) 和门控循环单元 (GRU) 与基于广义对立的学习粒子群优化算法 (GOBLPSO) 相结合的新型预测模型具有被提出来提高 DO 的预测精度。在建模过程中通过CEEMDAN-LZC方法将DO含量数值序列分解并重构为若干新特征。构建独立模型以拟合使用 GRU 获得的上述组件，并叠加预测值得到最终结果。此外，时间步长参数t和单元数参数u对 GRU 的性能有显着影响，由 GOBLPSO 算法选择。基于河蟹养殖池塘DO含量数据的模拟结果表明，该模型具有良好的本质特征提取能力，对水产养殖DO含量预测非常强大和可靠。