Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach

Underground building spaces such as metro stations have been widely adopted in densely populated metropolitan cities to combat high traffic congestion. To ensure a sustainable underground environment, early warning systems, primarily for fine particulate matter, and ventilation control, are of great significance. However, these techniques require substantial sensor data, which is generally not accessible in real-life situations. This work presents a solution to sensor data unavailability at underground metro stations by integrating a sequence forecasting soft-sensor framework with zero-shot transfer learning (ZSTL). The proposed data-driven soft-sensor model consists of a multi-head attention aware bi-directional gated recurrent unit (MH-BiGRU) that forecasts the PM_2.5 one hour ahead of time to provide an early health risk warning and proactive ventilation operation. The results suggest that the forecasts by the proposed model outperform stacked long short-term memory and gated recurrent unit structures by 17% and 20%, respectively. Whereas 72% of the residual errors by MH-BiGRU were situated between −5 to 5 CIAI units. Additionally, the proposed ZSTL framework reduced the zero-shot prediction error by 75%, 17%, and 82% at three target metro stations, where the data of target sensor PM_2.5 was not available. The ventilation control by forecasted PM_2.5 levels can decrease indoor PM_2.5 concentrations by 25% and has the potential to reduce energy demand by 18%, enabling sustainable operation.