A deep multitask learning approach for air quality prediction

Xiaotong Sun, Wei Xu (), Hongxun Jiang and Qili Wang
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Xiaotong Sun: Renmin University of China
Wei Xu: Renmin University of China
Hongxun Jiang: Renmin University of China
Qili Wang: Renmin University of China

Annals of Operations Research, 2021, vol. 303, issue 1, No 4, 79 pages

Abstract: Abstract Air pollution is one of the most serious threats to human health and is an issue causing growing public concern. Air quality forecasts play a fundamental role in providing decision-making support for environmental governance and emergency management, and there is an imperative need for more accurate forecasts. In this paper, we propose a novel spatial–temporal deep multitask learning (ST-DMTL) framework for air quality forecasting based on dynamic spatial panels of multiple data sources. Specifically, we develop a prediction model by combining multitask learning techniques with recurrent neural network (RNN) models and perform empirical analyses to evaluate the utility of each facet of the proposed framework based on a real-world dataset that contains 451,509 air quality records that were generated on an hourly basis from January 2013 to September 2017 in China. An application check is also conducted to verify the practical value of our proposed ST-DMTL framework. Our empirical results indicate the efficacy of the framework as a viable approach for air quality forecasts.

Keywords: Air quality forecast; Multitask learning; Deep learning; Spatial–temporal analysis; Smart city (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10479-020-03734-1

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