Distributed Deep Features Extraction Model for Air Quality Forecasting

Axel Gedeon Mengara Mengara, Younghak Kim, Younghwan Yoo and Jaehun Ahn
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Axel Gedeon Mengara Mengara: School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea
Younghak Kim: School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea
Younghwan Yoo: School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea
Jaehun Ahn: School of Urban, Architecture and Civil Engineering, Pusan National University, Busan 46241, Korea

Sustainability, 2020, vol. 12, issue 19, 1-19

Abstract: Several studies in environmental engineering emphasize the importance of air quality forecasting for sustainable development around the world. In this paper, we studied a new approach for air quality forecasting in Busan metropolitan city. We proposed a convolutional Bi-Directional Long-Short Term Memory (Bi-LSTM) autoencoder model trained using a distributed architecture to predict the concentration of the air quality particles ( PM 2.5 and PM 10 ). The proposed deep learning model can automatically learn the intrinsic correlation among the pollutants in different location. Also, the meteorological and the pollution gas information at each location are fully utilized, which is beneficial for the performance of the model. We used multiple one-dimension convolutional neural network (CNN) layers to extract the local spatial features and a stacked Bi-LSTM layer to learn the spatiotemporal correlation of air quality particles. In addition, we used a stacked deep autoencoder to encode the essential transformation patterns of the pollution gas and the meteorological data, since they are very important for providing useful information that can significantly improve the prediction of the air quality particles. Finally, in order to reduce the training time and the resource consumption, we used a distributed deep leaning approach called data parallelism , which has never been used to tackle the problem of air quality forecasting. We evaluated our approach with extensive experiments based on the data collected in Busan metropolitan city. The results reveal the superiority of our framework over ten baseline models and display how the distributed deep learning model can significantly improve the training time and even the prediction accuracy.

Keywords: air quality prediction; deep learning; PM 2.5; PM 10; Busan city; distributed model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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