Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards

As the distribution of indoor contaminants is often heterogeneous, the traditional Wells–Riley equation is inadequate for accurately assessing the infection risk to indoor personnel. In this study, contaminant concentration data from hospital wards were obtained through experimentally validated computational fluid dynamics (CFD) simulations. Four common machine learning models—multiple linear regression (MLR), support vector regression (SVR), backpropagation (BP) neural network, and convolutional neural network (CNN)—were employed to predict the distribution of contaminants within the wards. The results demonstrate that the CNN achieves the best predictive performance, followed by the BP neural network. Specifically, the CNN exhibited a root mean square error, mean absolute error, and mean absolute percentage error of 6.31 ppm, 3.18 ppm, and 8.33%, respectively. Comparative analysis revealed that the CNN reduced the mean absolute percentage error (MAPE) by 58.18%, 33.47%, and 25.15% compared to MLR, SVR, and BPNN, respectively.