Long-term multivariate water quality forecasting for sustainable aquaculture management

Abstract

Accurate water quality prediction is essential for intelligent aquaculture management, enabling timely intervention, risk mitigation, and sustainable resource use. Key parameters such as dissolved oxygen, chlorophyll-a, and pH are influenced by complex spatiotemporal dynamics, making long-term forecasting particularly challenging in high-density aquaculture systems. Traditional methods struggle to balance local details and global trends, while circadian rhythms, feeding cycles, and seasonal shifts cause dynamic dependencies and distribution drift. To address these issues, we propose a novel deep learning framework with three core components: (1) a multi-scale decomposition module with time–frequency enhancement, which removes cross-scale redundancy, suppresses noise, and integrates local–global features via hierarchical decomposition and feature reorganization; (2) an adaptive sequence perception attention mechanism based on graph learning, which captures dynamic variable dependencies and models spatiotemporal interactions, including environmental coupling and aquaculture disturbances; and (3) a GRU-MoE network with a dynamic expert selection strategy that adjusts to data characteristics, mitigating distribution drift caused by human interventions like feeding and oxygenation. Extensive experiments on four real-world water quality datasets show the proposed method outperforms six deep learning baselines, achieving an average MAE reduction of 53.17%, RMSE reduction of 51.68%, $R^2$ improvement of 0.4945, and KGE improvement of 0.1979. Furthermore, Kolmogorov–Smirnov test results confirm the model’s ability to recover real data distributions and their temporal evolution. This high-precision long-term prediction method enhances aquaculture system resilience, reduces risks from water quality fluctuations, and provides a robust foundation for informed decision-making and sustainable aquaculture management.