A Symmetry-Driven Hybrid Framework Integrating ITTAO and sLSTM-Attention for Air Quality Prediction

Air pollution poses a threat to public health, ecosystem stability, and sustainable development. Accurate air quality prediction is essential for environmental protection and achieving sustainability. This study proposes a symmetry-driven hybrid framework that integrates an Improved Triangulation Topology Aggregation Optimizer (ITTAO) with a Stable Long Short-Term Memory (sLSTM) network and an attention mechanism to achieve high-precision air quality prediction. Three enhancement strategies are introduced to improve the optimization capability of the TTAO algorithm. Experiments with CEC2017 standard functions validate the ITTAO algorithm’s superior convergence and global search ability. ITTAO then optimizes the hyperparameters of the sLSTM-Attention model, resulting in the ITTAO-sLSTM-Attention model. Four air quality datasets from diverse regions in China verify the model’s performance, demonstrating that the proposed model outperforms seven swarm intelligence-optimized sLSTM-Attention models and six machine learning models. Compared to the LSTM model, ITTAO-sLSTM-Attention reduces RMSE by 23.47%, 13.23%, 19.69%, and 26.46% across four cities, confirming its enhanced accuracy and generalization. Finally, an interactive air quality prediction system based on the ITTAO-sLSTM-Attention model and PyQt is developed, offering a user-friendly tool for air quality prediction.