Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction

• Main Authors: Fu, Minglei, Le, Caowei, Fan, Tingchao, Prakapovich, Ryhor, Manko, Dmytro, Dmytrenko, Oleh, Lande, Dmytro, Shahid, Shamsuddin, Yaseen, Zaher Mundher
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2021
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/94129/; http://dx.doi.org/10.1007/s11356-021-15574-y
• Institution: Universiti Teknologi Malaysia

The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data serie...