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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yi-Chung Chen & Tsu-Chiang Lei & Shun Yao & Hsin-Ping Wang, 2020. "PM2.5 Prediction Model Based on Combinational Hammerstein Recurrent Neural Networks," Mathematics, MDPI, vol. 8(12), pages 1-23, December.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2178-:d:457681
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