NDLI: Attention-Based Distributed Deep Learning Model for Air Quality Forecasting

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Attention-Based Distributed Deep Learning Model for Air Quality Forecasting

Content Provider	MDPI
Author	Mengara, Axel Gedeon Mengara Park, Eunyoung Jang, Jinho Yoo, Younghwan
Copyright Year	2022
Abstract	Air quality forecasting has become an essential factor in facilitating sustainable development worldwide. Several countries have implemented monitoring stations to collect air pollution particle data and meteorological information using parameters such as hourly timespans. This research focuses on unravelling a new framework for air quality prediction worldwide and features Busan, South Korea as its model city. The paper proposes the application of an attention-based convolutional BiLSTM autoencoder model. The proposed deep learning model has been trained on a distributed framework, referred to data parallelism, to forecast the intensity of particle pollution ( $P M_{2.5}$ and $P M_{10}$ ). The algorithm automatically learns the intrinsic correlation among the particle pollution in different locations. Each location’s meteorological and traffic data is extensively exploited to improve the model’s performance. The model has been trained using air quality particle data and car traffic information. The traffic information is obtained by a device which counts cars passing a specific area through the YOLO algorithm, and then sends the data to a stacked deep autoencoder to be encoded alongside the meteorological data before the final prediction. In addition, multiple one-dimensional CNN layers are used to obtain the local spatial features jointly with a stacked attention-based BiLSTM layer to figure out how air quality particles are correlated in space and time. The evaluation of the new attention-based convolutional BiLSTM autoencoder model was derived from data collected and retrieved from comprehensive experiments conducted in South Korea. The results not only show that the framework outperforms the previous models both on short- and long-term predictions but also indicate that traffic information can improve the accuracy of air quality forecasting. For instance, during $P M_{2.5}$ prediction, the proposed attention-based model obtained the lowest MAE (5.02 and 22.59, respectively, for short-term and long-term prediction), RMSE (7.48 and 28.02) and SMAPE (17.98 and 39.81) among all the models, which indicates strong accuracy between observed and predicted values. It was also found that the newly proposed model had the lowest average training time compared to the baseline algorithms. Furthermore, the proposed framework was successfully deployed in a cloud server in order to provide future air quality information in real time and when needed.
Starting Page	3269
e-ISSN	20711050
DOI	10.3390/su14063269
Journal	Sustainability
Issue Number	6
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2022-03-10
Access Restriction	Open
Subject Keyword	Sustainability Transportation Science and Technology Air Quality Forecasting Deep Learning Models Particle Pollution Busan Metropolitan City Data Parallelism Architecture
Content Type	Text
Resource Type	Article

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