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JCSIS

Deep Learning and Enhanced Emissions Modeling and Deposition Prediction

Sam M. K. Weiguo Gee Narcisa Zlatan Khadija Shazly
Volume 3

Abstract

In recent years, the issue of pollution in the air has emerged as one of the most pressing concerns
facing our society.
It is vital to predict air pollution in order to provide early warnings to the community
and prevent the pollution from impacting the public.
In this study, we developed an AI model for predicting PM2.5 concentrations 2 hours in advance
by utilizing ensemble empirical mode decomposition in conjunction with long-short
term memory.
Our approach primarily consists of two stages: the first stage involves breaking down time-
series data into intrinsic features, and the second stage involves using a model with long-short
term memory to learn patterns from training data.
The results of the experiments reveal that our work performs much better than the baseline mode
ls and greatly improves performance in terms of RMSE and IOA.

Keywords

deep learning , ensemble empirical mode decomposition , long-short term memory ,air pollution prediction

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