DAerosol-NTM: applying deep learning and neural Turing machine in aerosol prediction

The pollution caused by aerosol (particulate matter) has a detrimental impact on urban environments, particularly in terms of socio-economic factors and public health. Aerosol particles, ranging in size from 1 nm to 100 µm, can easily penetrate organic tissues, carrying toxic gaseous compounds and minerals such as carbon monoxide, ozone, nitrogen dioxide, and sulfur dioxide. Recent advancements in neural network technology, combined with deep learning techniques, have made it possible to predict surges in aerosol pollution. In this study, we introduce DAerosol-NTM, a deep learning framework that utilizes the latest developments in neural Turing machines (NTMs) to access external memory. When compared with four baseline studies that employ multilayer perceptron (MLP), deep neural networks (DNNs), long short-term memory (LSTM), and deep LSTM (DLSTM), DAerosol-NTM significantly improves prediction accuracy by 8–31% and precision by 46–91% and reduces the root mean square error (RMSE) by 24–85%. Additionally, DAerosol-NTM incorporates up to 20 years of particulate matter data in its external storage, making it the first model capable of predicting aerosol pollution surges. By analyzing the data from the previous 96 h, the optimal time interval before and after the aerosol event (TIBAAE) enables the prediction of aerosol events within the following 24 h.