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Open AccessArticle

Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction

by Xiaotong Sun and Wei Xu *
School of Information, Renmin University of China, Beijing 100872, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(9), 560; https://doi.org/10.3390/atmos10090560
Received: 30 June 2019 / Revised: 11 August 2019 / Accepted: 23 August 2019 / Published: 18 September 2019
(This article belongs to the Special Issue Air Quality Control and Planning)
Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) framework for air quality forecasting. Specifically, we incorporate real-time pollutant emission data into the model input. We also design a spatial-temporal analysis approach to make good use of these data. The prediction model is developed by combining random subspace learning with a deep learning algorithm in order to improve the prediction accuracy. Empirical analyses based on multiple datasets over China from January 2015 to September 2017 are performed to demonstrate the efficacy of the proposed framework for hourly pollutant concentration prediction at an urban-agglomeration scale. The empirical results indicate that our framework is a viable method for air quality prediction. With consideration of the regional scale, the LSTM-DRSL framework performs better at a relatively large regional scale (around 200–300 km). In addition, the quality of predictions is higher in industrial areas. From a temporal point of view, the LSTM-DRSL framework is more suitable for hourly predictions. View Full-Text
Keywords: air quality prediction; random subspace learning; deep learning; spatial-temporal analysis; smart city air quality prediction; random subspace learning; deep learning; spatial-temporal analysis; smart city
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Sun, X.; Xu, W. Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction. Atmosphere 2019, 10, 560.

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