AIR POLLUTANTS CONCENTRATION PREDICTION BASED ON TRANSFER LEARNING AND RECURRENT NEURAL NETWORK

Air pollution poses a great threat to human health, and people are paying more and more attention to the prediction of air pollution. Prediction of air pollution helps people plan for their outdoor activities and helps protect human health. In this article, long-short term memory recurrent neural networks were used to predict the future concentration of air pollutants in Macau. In addition, meteorological data and data on the concentration of air pollutants were used. Moreover, in Macau, some air quality monitoring stations have less observed data, and some AQMSs less observed data of certain types of air pollutants. Therefore, the transfer learning and pre-trained neural networks were used to assist AQMSs with less observed data to generate neural network with high prediction accuracy. In this thesis, in most cases, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy, used less training time than randomly initialized recurrent neural networks.