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Air Pollution Source Identification by Using Neural Network with Bayesian Optimization

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Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 994))

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Abstract

To accurately locate pollution sources, in this research, we first create an air-pollution identification system, called Air Pollution Source Identification System (APSIS), which adopts tensorflow to establish three neural-network based analytical models with which to find the sources of air pollution. The APSIS collects environmental data in a relatively smaller grid area. Next, collected data are tuned when necessary to prevent the APSIS built by collected data from being seriously affected by outlier and other unstable factors, like wind direction. The purpose is to identify possible distribution of pollution and then more accurately find out the sources. After that, the APSIS is applied to identify the sources of air pollution in a wide area. Source identification accuracies of these neural networks are compared with other air diffusion models, aiming to develop one which is suitable for identifying the air pollution sources.

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Author information

Authors and Affiliations

  1. Computer Science Department, Tunghai University, Taichung, Taiwan

    Fang-Yie Leu & Jia-Sheng Ho

Authors
  1. Fang-Yie Leu
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  2. Jia-Sheng Ho
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Corresponding author

Correspondence to Fang-Yie Leu .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  3. School of Business, University of New South Wales, Canberra, ACT, Australia

    Omar K. Hussain

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Leu, FY., Ho, JS. (2020). Air Pollution Source Identification by Using Neural Network with Bayesian Optimization. In: Barolli, L., Xhafa, F., Hussain, O. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2019. Advances in Intelligent Systems and Computing, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-22263-5_49

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