Kernel PLS with AdaBoost ensemble learning for particulate matters forecasting in subway environment

Excessive exposure to hazardous air pollutants remains the leading cause of serious cardio-respiratory disease among commuters. To monitor and improve the air quality in time, soft-sensing techniques in metro systems need to be introduced. Adaptive boosting (AdaBoost), as an ensemble learning method, has greater flexibility and adaptability. Compared with a single conventional model, it has a stronger capability to explain the complex features contained in air quality data. In this study, an improved scheme employs AdaBoost to predict key air quality variables, such as PM2.5, in subway environment. To eliminate the adverse effects of noise interference and the collinearity problem among data variables, kernel latent variables are embedded into the original AdaBoost. The proposed method combines kernel partial least squares with AdaBoost (KPLS-AdaBoost). Root mean square error (RMSE) was used to evaluate the prediction performance of KPLS-AdaBoost. The results demonstrate that KPLS-AdaBoost could achieve better modeling performance, providing a new way to realize real-time monitoring of hazardous air pollutants. More precisely, in terms of PM2.5 in the hall, the RMSE value was optimized by approximately 14.44-31.86% compared with that of AdaBoost, random forest, artificial neural network, PLS, KPLS, and PLS-AdaBoost. For PM2.5 at the platform, RMSE value can be decreased by 13.57-38.51%.

Automated summary

Excessive exposure to hazardous air pollutants is the main cause of serious cardio-respiratory diseases among commuters. In order to monitor and improve the air quality in metro systems, this study introduces the KPLS-AdaBoost method to predict key air quality variables. The results demonstrate that KPLS-AdaBoost achieves better modeling performance, providing a new way for real-time monitoring of hazardous air pollutants.