Sustainable Sewage Treatment Prediction Using Integrated KAN-LSTM with Multi-Head Attention

The accurate prediction of sewage treatment indicators is crucial for optimizing management and supporting sustainable water use. This study proposes the KAN-LSTM model, a hybrid deep learning model combining Long short-term memory (LSTM) networks, Kolmogorov-Arnold Network (KAN) layers, and multi-head attention. The model effectively captures complex temporal dynamics and nonlinear relationships in sewage data, outperforming conventional methods. We applied correlation analysis with time-lag consideration to select key indicators. The KAN-LSTM model then processes them through LSTM layers for sequential dependencies, KAN layers for enhanced nonlinear modeling via learnable B-spline transformations, and multi-head attention for dynamic weighting of temporal features. This combination handles short-term patterns and long-range dependencies effectively. Experiments showed the model’s superior performance, achieving 95.13% R-squared score for FOss (final sedimentation basin outflow suspended solid, one indicator of our research predictions)and significantly improving prediction accuracy. These advancements in intelligent sewage treatment prediction modeling not only enhance water sustainability but also demonstrate the transformative potential of hybrid deep learning approaches. This methodology could be extended to optimize predictive tasks in sustainable aquaponic systems and other smart aquaculture applications.