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Adaptive-neuro fuzzy inference trained with PSO for estimating the concentration and severity of sulfur dioxiderelease

Author

Listed:
  • Mourad Achouri

    (Université 20 Aout 1955 Skikda)

  • Youcef Zennir

    (Université 20 Aout 1955 Skikda)

  • Cherif Tolba

    (Badji Mokhtar University)

  • Fares Innal

    (Université 20 Aout 1955 Skikda)

  • Chaima Bensaci

    (Université 20 Aout 1955 Skikda)

  • Yiliu Liu

    (NTNU University)

Abstract

The main purpose of this study is to propose a decision support system that deals with the uncertainties in a model of atmospheric dispersion and in meteorological data (speed and direction of wind), which may negatively affect the model accuracy. This later helps the safety agencies in making decisions and allocating necessary materials and human resources to handle potential disastrous events. In order to investigate the aforementioned issues and provide a more reliable data we propose the adaptive Neuro-Fuzzy inference (ANFIS) system enhanced by the mean particle swarm optimization (PSO) to predict the concentration of Sulfur Dioxide release in the atmosphere. This method takes the advantages of fuzzy logic system to address the uncertainties and the ability of neural network to learn from the data. Furthermore our study attempts to estimate the severity index of the released material with the help of fuzzy logic. The result of our study shows that the presented method is successfully applied and it can be a powerful alternative to deal with Sulfur Dioxide release.

Suggested Citation

  • Mourad Achouri & Youcef Zennir & Cherif Tolba & Fares Innal & Chaima Bensaci & Yiliu Liu, 2024. "Adaptive-neuro fuzzy inference trained with PSO for estimating the concentration and severity of sulfur dioxiderelease," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 3279-3292, July.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02336-5
    DOI: 10.1007/s13198-024-02336-5
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