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A hybrid neuro-fuzzy prediction system with butterfly optimization algorithm for PM2.5 forecasting

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Abstract

With the rapid increase of urbanization and industrialization, particulate matter (PM2.5) concentration has increased significantly. PM2.5 profile forecasting has become one of the critical research areas in environmental control and protection. The early detection of PM2.5 as a pollutant is vital because PM2.5 has a significant impact on human health than other pollutants. This paper proposes a deep neuro-fuzzy prediction system (DNFPS) by amalgamating the deep learning and the fuzzy time series algorithm to forecast the PM2.5 concentration. The proposed predictive model consists of three phases; a data preprocessing algorithm to generate a high-quality dataset, a denoising autoencoder using fully convolutional neural networks (FCNNs) to extract the features from the pollutant time series profile as well as reduce the dimension of the time series dataset, and the type-2 fuzzy time series forecasting (FTSF) method to forecast PM2.5 concentration. The butterfly optimization algorithm (BOA) is integrated with the type-2 FTSF method to improve the prediction accuracy of the proposed method. FTSF-BOA is implemented to fine-tune the length of type-2 fuzzy intervals. Experiments employing Sydney data sets to analyze the performance of DNFPS. DNFPS shows that the proposed model achieves an excellent performance than other standard baseline models. It has lower computational time (training time) than the other traditional baseline deep learning models.

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  • 04 April 2022

    The original version of this article was revised to update the spelling mistake in the title and the bracket in the refence citation.

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Author notes

  1. Samit Bhanja, Santanu Metia and Abhishek Das have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, Government General Degree College, Singur, Singur, Hooghly, 712409, West Bengal, India

    Samit Bhanja

  2. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Santanu Metia

  3. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Santanu Metia

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Santanu Metia

  5. Department of Computer Science and Engineering, Aliah University, New Town, Kolkata, West Bengal, India

    Samit Bhanja & Abhishek Das

Authors
  1. Samit Bhanja
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  2. Santanu Metia
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  3. Abhishek Das
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Contributions

SB: conceptualization, methodology, formal analysis, writing original draft; SM: data collection, methodology, writing-review and editing; AD: conceptualization, supervision, writing -review and editing.

Corresponding author

Correspondence to Abhishek Das.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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The original version of this article was revised to update the spelling mistake in the title and the bracket in the refence citation.

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Bhanja, S., Metia, S. & Das, A. A hybrid neuro-fuzzy prediction system with butterfly optimization algorithm for PM2.5 forecasting. Microsyst Technol 28, 2577–2592 (2022). https://doi.org/10.1007/s00542-022-05252-5

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  • DOI: https://doi.org/10.1007/s00542-022-05252-5

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