PM2.5 Prediction Model Based on Combinational Hammerstein Recurrent Neural Networks

Yi-Chung Chen, Tsu-Chiang Lei, Shun Yao and Hsin-Ping Wang
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Yi-Chung Chen: Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, College of Management, Main Campus, Yunlin 64002, Taiwan
Tsu-Chiang Lei: Feng-Chia University, Taichung 40724, Taiwan
Shun Yao: Feng-Chia University, Taichung 40724, Taiwan
Hsin-Ping Wang: Feng-Chia University, Taichung 40724, Taiwan

Mathematics, 2020, vol. 8, issue 12, 1-23

Abstract: Airborne particulate matter 2.5 (PM2.5) can have a profound effect on the health of the population. Many researchers have been reporting highly accurate numerical predictions based on raw PM2.5 data imported directly into deep learning models; however, there is still considerable room for improvement in terms of implementation costs due to heavy computational overhead. From the perspective of environmental science, PM2.5 values in a given location can be attributed to local sources as well as external sources. Local sources tend to have a dramatic short-term impact on PM2.5 values, whereas external sources tend to have more subtle but longer-lasting effects. In the presence of PM2.5 from both sources at the same time, this combination of effects can undermine the predictive accuracy of the model. This paper presents a novel combinational Hammerstein recurrent neural network (CHRNN) to enhance predictive accuracy and overcome the heavy computational and monetary burden imposed by deep learning models. The CHRNN comprises a based-neural network tasked with learning gradual (long-term) fluctuations in conjunction with add-on neural networks to deal with dramatic (short-term) fluctuations. The CHRNN can be coupled with a random forest model to determine the degree to which short-term effects influence long-term outcomes. We also developed novel feature selection and normalization methods to enhance prediction accuracy. Using real-world measurement data of air quality and PM2.5 datasets from Taiwan, the precision of the proposed system in the numerical prediction of PM2.5 levels was comparable to that of state-of-the-art deep learning models, such as deep recurrent neural networks and long short-term memory, despite far lower implementation costs and computational overhead.

Keywords: feature selection; recurrent neural networks; PM2.5 predictions; time series prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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