Open AccessAN AIR QUALITY FORECASTING MODEL BASED ON BIDIRECTIONAL GRU AND LSTM
Author(s) -
Veena Aneja,
Rajeev K. Singh
Publication year - 2021
Publication title -
international research journal of computer science
Language(s) - English
Resource type - Journals
ISSN - 2393-9842
DOI - 10.26562/irjcs.2021.v0809.001
Subject(s) - air quality index , deep learning , artificial intelligence , computer science , air pollution , artificial neural network , government (linguistics) , quality (philosophy) , machine learning , sustainable development , meteorology , geography , political science , linguistics , chemistry , philosophy , organic chemistry , epistemology , law
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.