A New Collaborative Multi‐Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment

Mustafa Hamid Hassan, Salama A. Mostafa*, Aida Mustapha, Mohd Zainuri Saringat, Bander Ali Saleh Al‐rimy, Faisal Saeed, A. E.M. Eljialy, Mohammed Ahmed Jubair

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Citations (SciVal)
    Original languageEnglish
    Article number510
    JournalSustainability (Switzerland)
    Volume14
    Issue number1
    DOIs
    Publication statusPublished (VoR) - 1 Jan 2022

    Funding

    The authors express appreciation to the Malaysia Ministry of Higher Education (MoHE). This research was funded by the Fundamental Research Grant Scheme (FRGS/1/2019/ICT04/UTHM/03/1) grant vot number K209.The authors would like to thank the Department Of Environment (DOE) for providing the required data and assistance for this work. The authors also would like to thank the Center of Intelligent and Autonomous Systems (CIAS) at the Faculty of Computer Science and Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia (UTHM) for supporting this work. Funding: The authors express appreciation to the Malaysia Ministry of Higher Education (MoHE). This research was funded by the Fundamental Research Grant Scheme (FRGS/1/2019/ICT04/UTHM/03/1) grant vot number K209.

    FundersFunder number
    Center of Intelligent and Autonomous Systems
    Department Of Environment
    FSKTM
    Faculty of Computer Science and Information Technology
    Universiti Tun Hussein Onn Malaysia
    Ministry of Higher Education, MalaysiaFRGS/1/2019/ICT04/UTHM/03/1

      Keywords

      • Air pollution
      • Air quality index
      • Autoregressive integrated moving average
      • Monte Carlo simulation
      • Multi‐agent system
      • Risk assessment

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