Surface water quality prediction via an MLA-Mamba hybrid neural network with GRPO optimization

Surface water quality forecasting is crucial for pollution early warning and sustainable water resource management. However, accurate prediction of key water quality indicators remains challenging due to the highly nonlinear spatio-temporal dynamics and complex inter-variable relationships. Traditional statistical models and conventional machine learning approaches often struggle to effectively capture these couplings, leading to limited predictive performance. In this study, we propose a novel hybrid deep learning framework, termed MLA-Mamba, which integrates an improved Mamba-based sequence modeling network with a Multi-Head Local Attention (MLA) mechanism, optimized through a Gradient Reparameterization Optimization (GRPO) strategy. The Mamba module is designed to extract long-range temporal dependencies from water quality time series via a state-space modeling paradigm, while the MLA mechanism captures localized spatial correlations among multiple monitoring stations. To the best of our knowledge, this study represents one of the first explorations of applying Gradient Reparameterization Optimization (GRPO) to water quality prediction tasks. Furthermore, a multi-task learning scheme is incorporated to jointly predict multiple key indicators, including permanganate index (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN), thereby exploiting inter-variable dependencies to enhance overall forecasting accuracy. The proposed GRPO strategy dynamically adjusts learning rates during training to accelerate convergence and improve model stability. Experimental evaluations on two real-world surface water datasets demonstrate that the proposed MLA-Mamba model achieves consistent performance improvements over the evaluated baseline methods across multiple error metrics. In addition, predictive uncertainty is quantified via Monte Carlo dropout, enabling the estimation of confidence intervals to support risk-aware water quality assessment. These results highlight the effectiveness of integrating advanced sequence modeling, attention-driven spatial feature extraction, and adaptive optimization for robust environmental time series forecasting.