Dynamic slow feature analysis and random forest for subway indoor air quality modeling

Abstract

The indoor air quality of a metro station is closely related to the health of passengers. In order to monitor the concentration of particulate matter in the air more stably and reduce the monitoring cost, a soft sensing model based on dynamic slow feature analysis (DSFA) and random forest (RF) is proposed in this paper. First, the augmented matrix technique is embedded into the slow feature analysis model to create a DSFA model to cope with the time lag inherent in the actual measurement process. Then, the constructed DSFA model is used to extract slowly varying latent variables to capture the dynamic trend within the input data. Finally, taking the latent variables as the input and aiming at the nonlinearity in the data, RF is used to predict the particle concentration. The results show that the prediction performance of the DSFA-RF model is better than some traditional soft sensor models, including partial least squares, support vector regression, single RF, and a hybrid model. The root mean square error value of DSFA-RF is improved by 21.93% compared with the hybrid model based on principal component analysis and support vector regression. In addition, DSFA also outperforms in health risk assessment.