AN AIR QUALITY FORECASTING MODEL BASED ON BIDIRECTIONAL GRU AND LSTM

Abstract

With the urban and industrial growth, many evolving countries suffer from excessive air pollution. The growing concern about air pollution has been raised by the government and people because it affects individual’s health and sustainable development globally. Several factors influence Air Quality and we must use all of them to interpose and forecast air pollution for the whole city. Thearticle suggested a novel deep learning CBGLSTM model for air quality forecasting based on 1D CNN, Bi-GRU, and Bi-LSTM neural networks. The model has experimented on the PM2.5 dataset taken from UCI Machine Learning Repository. The results demonstrate that compared to other traditional deep learning models, the CBGLSTM model gives better prediction performance and less error.