PM_2.5 Fine-grained Air Quality Prediction Model Based on Spatial-Temporal Cognitive Dilated Convolution Network and Multi-source Influencing Factors

To achieve accurate and fine-grained PM_2.5 concentration prediction, this paper proposed a PM_2.5 concentration prediction model based on spatial-temporal cognitive dilated convolution network (ST-C-DCN). The model applied spatial-temporal factors and meteorological factors to PM_2.5 concentration prediction, extracted spatial-temporal features based on causal convolution network, and optimized the extraction of spatial-temporal features by using spatial-temporal attention mechanism. Experimental results based on Haikou air pollution data show that for a single monitoring station, compared with the baseline model, the RMSE of ST-C-DCN decreases by 24.7% on average, the MAE decreases by 9.93% on average, and the R² increases by 3.35% on average. ST-C-DCN outperforms other models in terms of prediction accuracy for all monitoring stations, achieving the highest scores in win-tie-loss experiments (including MSE, RMSE, MAE, and R²), with values of 68, 63, and 64, respectively. The Friedman test conducted under different data sampling conditions confirms that ST-C-DCN exhibits significant performance improvement compared to the baseline model. In conclusion, ST-C-DCN provides a potential direction for fine-grained PM_2.5 prediction.