Development and comparison of regression models and feedforward backpropagation neural network models to predict seasonal indoor PM2.5–10 and PM2.5 concentrations in naturally ventilated schools

A combination of multivariate statistical methods, including multiple linear regression (MLR) and feedforward backpropagation (FFBP) were used to evaluate the influence of seasons on the concentrations of indoor PM2.5−10 and PM2.5 in twelve naturally ventilated schools located in Gaza Strip, Palestine. Samples were collected by using hand held particulate matter sampler during fall, winter and spring from 2011 to 2012. Statistical results revealed that MLR models agree fairly well with the measured data with reasonable coefficients of determination (R2) 0.58, 0.69 and 0.70 for indoor PM2.5 and 0.44, 0.56 and 0.57 for indoor PM2.5−10 during fall, winter and spring, respectively. The FFBP model results performed better than the MLR analysis in determining indoor PMs with R2 values of 0.75, 0.78 and 0.79 for PM2.5 and 0.65, 0.73, and 0.78 for PM2.5−10 during fall, winter, and spring, respectively. The accuracy (R2) models of the FFBP showed an improvement of 12.08%–25.56% and from 26.36% to 38.53% for prediction of indoor PM2.5 and PM2.5−10 compared to MLR models. In addition, FFBP models improved the accuracy by reducing the error (RMSE) as much as 19.35%, 7.41%, and 7.41% during fall, winter, and spring for prediction of indoor PM2.5, and by 32.00%, 7.41%, and 32.00% during fall, winter, and spring, respectively for prediction of indoor PM2.5−10 compared with MLR. Results showed that the artificial neural network approach can be capable of accurately modeling indoor air quality in naturally ventilated buildings.