PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model

The concentration of fine particulate matter (PM2.5), which represents inhalable particles with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can penetrate deep into the human lungs and severely affect human health. This paper studies accurate PM2.5 predictio...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.57338-57350
Main Authors: Nguyen, Minh Hieu, Le Nguyen, Phi, Nguyen, Kien, Le, Van An, Nguyen, Thanh-Hung, Ji, Yusheng
Format: Article
Language:English
Subjects:
Air quality
Atmospheric modeling
Coders
Decoding
encoder-decoder model
Encoders-Decoders
Feature extraction
Feature selection
genetic algorithm
Genetic algorithms
long short-term memory
Micrometers
Model accuracy
Outdoor air quality
Outliers (statistics)
Particulate emissions
PM 25
Predictive models
Statistics
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Summary:The concentration of fine particulate matter (PM2.5), which represents inhalable particles with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can penetrate deep into the human lungs and severely affect human health. This paper studies accurate PM2.5 prediction, which can potentially contribute to reducing or avoiding the negative consequences. Our approach's novelty is to utilize the genetic algorithm (GA) and an encoder-decoder (E-D) model for PM2.5 prediction. The GA benefits feature selection and remove outliers to enhance the prediction accuracy. The encoder-decoder model with long short-term memory (LSTM), which relaxes the restrictions between the input and output of the model, can be used to effectively predict the PM2.5 concentration. We evaluate the proposed model on air quality datasets from Hanoi and Taiwan. The evaluation results show that our model achieves excellent performance. By merely using the E-D model, we can obtain more accurate (up to 53.7%) predictions than those of previous works. Moreover, the GA in our model has the advantage of obtaining the optimal feature combination for predicting the PM2.5 concentration. By combining the GA-based feature selection algorithm and the E-D model, our proposed approach further improves the accuracy by at least 13.7%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3072280