Double-task physics-informed neural network for the prediction of PM2.5 concentration

Predicting values of Particulate Matter concentration presents an undeniable interest, as it is key in preventing their adversarial impact on human health. Physics-based methods rely on several different variables and equations to perform this task. They are reliable, as they are based on known physical laws and models. Neural networks rely on high computing power and an important volume of data. They are faster than physics-based models, but sometimes considered as black boxes and potentially less reliable. There is therefore a need for a fast and reliable solution. Physics-Informed Neural Networks make use of all four (variables, equations, computing power and a high volume of data), which makes them more reliable than classical neural networks, and faster than physics-based methods. This paper proposes a model that predicts Particulate Matter concentration values using a variety of meteorological and optical variables as well as a Physics-Inspired loss function. The impact of different factors on the performance of this model, such as the amount of available ground truth, the use of Boundary Conditions, and the prediction time frame, is discussed in this paper as well. The model shows satisfying performance. More precisely, when compared to the best baseline method presented, our model shows a diminution of the MAE from 3.74 to 2.71 .