Improved spatio-temporal graph convolutional network for forecasting of PM2.5 concentrations in underground powerhouse caverns group

Abstract

Accurate prediction of PM2.5 in underground powerhouse caverns group (UPCG) is of great significance for safeguarding the health of construction personnel and optimizing ventilation energy consumption. However, existing studies on PM2.5 prediction have not considered the impact of wind fields on the diffusion of PM2.5 or the spatio-temporal multi-scale information, which limits the accuracy of prediction models. To address these issues, this study proposed an improved Spatio-Temporal Graph Convolutional Network (STGCN) for predicting PM2.5 in UPCG under construction ventilation. Specifically, skip connections were incorporated between two feature pyramids to extract multi-scale spatio-temporal information from environmental features. Then, the PM2.5 diffusion distance map based on the Gaussian diffusion model was proposed as the adjacency matrix. Additionally, a Transformer-based spatio-temporal block fusion model was proposed to build a more efficient STGCN. The results demonstrate that the proposed model achieves smaller MAE and RMSE, as well as superior R², compared to Transformer and CNN-LSTM in both single-step and multi-step prediction tasks. Ablation experiments confirmed the effectiveness of each proposed module. The model accurately predicts PM2.5 concentrations in UPCG, providing reliable support for ventilation requirements regarding decision-making.