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RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration

Received: 26 June 2018     Accepted: 16 July 2018     Published: 9 August 2018
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Abstract

The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.

Published in American Journal of Biological and Environmental Statistics (Volume 4, Issue 2)
DOI 10.11648/j.ajbes.20180402.13
Page(s) 66-73
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Air Pollution, Sensor, Gaussian Plume Diffusion Model, Intelligent Optimized Algorithm

References
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Cite This Article
  • APA Style

    Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu. (2018). RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. American Journal of Biological and Environmental Statistics, 4(2), 66-73. https://doi.org/10.11648/j.ajbes.20180402.13

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    ACS Style

    Zheng Xipeng; Yang Shunsheng; Xiang Wenchuan; Chen Yu. RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. Am. J. Biol. Environ. Stat. 2018, 4(2), 66-73. doi: 10.11648/j.ajbes.20180402.13

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    AMA Style

    Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu. RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. Am J Biol Environ Stat. 2018;4(2):66-73. doi: 10.11648/j.ajbes.20180402.13

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  • @article{10.11648/j.ajbes.20180402.13,
      author = {Zheng Xipeng and Yang Shunsheng and Xiang Wenchuan and Chen Yu},
      title = {RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration},
      journal = {American Journal of Biological and Environmental Statistics},
      volume = {4},
      number = {2},
      pages = {66-73},
      doi = {10.11648/j.ajbes.20180402.13},
      url = {https://doi.org/10.11648/j.ajbes.20180402.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20180402.13},
      abstract = {The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration
    AU  - Zheng Xipeng
    AU  - Yang Shunsheng
    AU  - Xiang Wenchuan
    AU  - Chen Yu
    Y1  - 2018/08/09
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ajbes.20180402.13
    DO  - 10.11648/j.ajbes.20180402.13
    T2  - American Journal of Biological and Environmental Statistics
    JF  - American Journal of Biological and Environmental Statistics
    JO  - American Journal of Biological and Environmental Statistics
    SP  - 66
    EP  - 73
    PB  - Science Publishing Group
    SN  - 2471-979X
    UR  - https://doi.org/10.11648/j.ajbes.20180402.13
    AB  - The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • School of Civil Engineering, Southwest Jiaotong University, Chengdu, China

  • School of Civil Engineering, Southwest Jiaotong University, Chengdu, China

  • School of Civil Engineering, Southwest Jiaotong University, Chengdu, China

  • Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China

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