Forecasting of bioaerosol concentration by a Back Propagation neural network model.

Journal: The Science of the total environment
PMID: 31783453

Abstract

Bioaerosol in the atmosphere plays a very important role in environment and public health. To forecast the bioaerosol concentration, the correlation between bioaerosol concentration and meteorological factors was discussed, and a Back Propagation (BP) neural network with Principal Component Analysis (PCA) method was utilized in this study. The proposed method works in three steps. The first step is to compute the correlation between bioaerosol concentration and meteorological factors, which consists of analyzing correlation and selecting meteorological factors applied to the study of forecast model. The second step is to use PCA analysis to reduce the dimensions of meteorological dataset. The third step is to use BP neural network, setting up, training BP neural network and proving the feasibility of forecast model included. The results of our model in forecasting bioaerosol concentration show 10.55% of average relative error, 2.80 pieces/L (pcs/L) of average absolute error, and 84.01 grade of forecast accuracy, providing a promising model for the forecasting of bioaerosol concentration.

Authors

  • Xiaonan Li
    Department of Biology and Chemistry, College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.
  • Xi Cheng
    Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA; Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology (OCICB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of HealthRockville, MD, USA.
  • Wenjian Wu
    Electronic Information School, Wuhan University, Wuhan, China.
  • Qinghua Wang
  • Zhaoyang Tong
    State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
  • Xiaoqing Zhang
    a College of Information Science and Technology , Donghua University , Shanghai , China.
  • Dahai Deng
    Meteorological Bureau of Changsha Municipality, Changsha 410205, China.
  • Yihe Li
    Department of Biology and Chemistry, College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China. Electronic address: yhli@nudt.edu.cn.

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