Forecasting of fine particulate matter based on LSTM and optimization algorithm

• Main Authors: Zaini N., Ahmed A.N., Ean L.W., Chow M.F., Malek M.A.
• Other Authors: 56905328500
• Format: Article
• Published: Elsevier Ltd 2024
• Subjects: Air pollution; Artificial intelligence; Deep learning; Optimization; Particulate matter; Air quality; Empirical mode decomposition; Energy utilization; Forecasting; Health risks; Learning algorithms; Long short-term memory; Particles (particulate matter); Population statistics; Sustainable development; Traffic congestion; Air pollution forecasting; Air quality monitoring stations; Fine particulate matter; Forecasting accuracy; Optimisations; Optimization algorithms; Particulate Matter; Performance; PM 2.5; Particle swarm optimization (PSO)
• Institution: Universiti Tenaga Nasional

Accurate air pollution forecasting may provide valuable information for urban planning to maintain environmental sustainability and reduce mortality risk due to health problems. The city with higher industrial activities, traffic congestion, population density, and energy consumption is most likely...