Transfer learning driven sequential forecasting and ventilation control of PM2.5 associated health risk levels in underground public facilities

Highlights: • A TL-ResNet was developed for sequential forecast of PM_2.5 CIAI levels. • Transfer learning was utilized to address the insufficient modeling data problem. • Transfer learning network was evaluated under different data availability scenarios. • TL-ResNet can provide up to 40% improvement compared to a stand-alone network. • Healthy IAQ environment was achieved using the ventilation control system. Particulate matter with aerodynamic diameter less than 2.5 µm (PM_2.5) has become a major public concern in closed indoor environments, such as subway stations. Forecasting platform PM_2.5 concentrations is significant in developing early warning systems, and regulating ventilation systems to ensure commuter health. However, the performance of existing forecasting approaches relies on a considerable amount of historical sensor data, which is usually not available in practical situations due to hostile monitoring environments or newly installed equipment. Transfer learning (TL) provides a solution to the scant data problem, as it leverages the knowledge learned from well-measured subway stations to facilitate predictions on others. This paper presents a TL-based residual neural network framework for sequential forecast of health risk levels traced by subway platform PM_2.5 levels. Experiments are conducted to investigate the potential of the proposed methodology under different data availability scenarios. The TL-framework outperforms the RNN structures with a determination coefficient (R²) improvement of 42.84%, and in comparison, to stand-alone models the prediction errors (RMSE) are reduced up to 40%. Additionally, the forecasted data by TL-framework under limited data scenario allowed the ventilation system to maintain IAQ at healthy levels, and reduced PM_2.5 concentrations by 29.21% as compared to stand-alone network.