High-accuracy PM prediction via mutual information filtering and Bayesian-Optimized Spatio-Temporal Convolutional Networks.

Air pollution, particularly fine particulate matter (PM), poses severe threats to human health and ecological sustainability, rendering accurate prediction of PM concentrations imperative for proactive public health interventions and evidence-based policy-making. While deep learning models like LSTM, GRU, and CNN are widely adopted for their robust modeling capacities, the direct use of raw, unfiltered data introduces feature redundancy. This not only prolongs training duration and elevates overfitting risks but also degrades prediction accuracy by complicating model convergence. To address these challenges, this paper presents an advanced PM prediction framework incorporating three key innovations.First, in contrast to conventional static threshold-based feature selection, a dynamic framework integrating Mutual Information (MI) and Adaptive Information Distance (AID) is proposed. By quantifying nonlinear feature correlations (via MI) and spatial redundancies (via AID), the framework adaptively prunes redundant inputs, thereby enhancing the information utility of the feature space for subsequent modeling.Second, a Bayesian optimizer guided by multimodal Gaussian distributions is designed to overcome the limitation of traditional unimodal optimization, which often stagnates at local optima. This optimizer explores multiple potential optima in the parameter space concurrently, enabling efficient global hyperparameter search and improving model robustness to noise.Third, an Information Screening-Enhanced Spatiotemporal Convolutional Network (MIBO-STCN) is introduced. Building on our prior STCN framework that integrates causal convolution and adaptive receptive fields, this architecture embeds an information screening layer to achieve synergistic optimization of spatiotemporal dependency modeling and redundancy reduction. Through this synergistic data preprocessing and optimization pipeline, the framework substantially boosts the prediction accuracy and accelerates convergence of the STCN model. Experimental results demonstrate that the proposed approach outperforms state-of-the-art models, exhibiting robust generalization capabilities in PM concentration forecasting across diverse scenarios.