Assessing the Potential of Artificial Intelligence (Artificial Neural Networks) in Predicting the Spatiotemporal Pattern of Wildfire-Generated PM_2.5 Concentration

To evaluate the health effects of wildfire smoke, it is crucial to identify reliable models, at fine spatiotemporal resolution, of exposure to wildfire-generated PM_2.5. To this end, satellite-drived aerosol optical depth (AOD) measurements are widely used in exposure models, providing long and short-term PM_2.5 predictions. Multiple regression models, specifically land use regression (LUR), incorporating AOD images have shown good potential for estimating long-term PM_2.5 exposure, but less so for short-term predictions. In this study, we developed artificial neural networks (ANNs) and, in particular, multilayer perceptron (MLP) by integrating ground-based PM_2.5 measurements with AOD images and meteorological and spatial variables. Moreover, we used spatial- and temporal-ANNs to investigate and compare the ANNs’ ability to predict different PM_2.5 concentration levels caused by abrupt spatial and temporal changes in fire smoke. The study herein analyzes and compares the viability of previously established neural network approaches in predicting short-term PM_2.5 exposure during the 2014–2017 wildfire seasons in the province of Alberta, Canada. The performance of ANNs is also compared to classical models, including simple correlation (PM_2.5 vs. AOD) and multiple linear regression (MLR) including meteorological and land-use predictors (MET_AOD_LUR). Our study shows that ANN achieved a 15% to 113% R² increase compared to competing models. View Full-Text