Journal
NEURAL NETWORKS
Volume 133, Issue -, Pages 157-165Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2020.10.013
Keywords
PM2.5 prediction; PLS; Temperature-based deep belief network; Structural optimization
Funding
- National Natural Science Foundation of China [62003185]
Ask authors/readers for more resources
Air quality prediction, specifically PM2.5, is a critical global issue. A novel deep learning model called temperature-based deep belief networks (TDBN) is proposed for PM2.5 prediction, outperforming similar models in terms of RMSE, MAE, and R-2.
Air quality prediction is a global hot issue, and PM2.5 is an important factor affecting air quality. Due to complicated causes of formation, PM2.5 prediction is a thorny and challenging task. In this paper, a novel deep learning model named temperature-based deep belief networks (TDBN) is proposed to predict the daily concentrations of PM2.5 for the next day. Firstly, the location of PM2.5 concentration prediction is Chaoyang Park in Beijing of China from January 1, 2018 to October 27, 2018. The auxiliary variables are selected as input variables of TDBN by Partial Least Square (PLS), and the corresponding data is divided into three independent sections: training samples, validating samples and testing samples. Secondly, the TDBN is composed of temperature-based restricted Boltzmann machine (RBM), where temperature is considered as an effective physical parameter in energy balance of training RBM. The structural parameters of TDBN are determined by minimizing the error in the training process, including hidden layers number, hidden neurons and value of temperature. Finally, the testing samples are used to test the performance of the proposed TDBN on PM2.5 prediction, and the other similar models are tested by the same testing samples for convenience of comparison with TDBN. The experimental results demonstrate that TDBN performs better than its peers in root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R-2). (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available