Using the MPFRL-WASPAS Model for the Selection of AI-Based Early Warning Systems Based on Natural Disaster Management and Frank Aggregation Operators

Authors

  • Hamza Zafar National Yunlin University of Science and Technology image/svg+xml Author
  • Zeeshan Ali National Yunlin University of Science and Technology image/svg+xml Author
  • Muhammad Asif National Yunlin University of Science and Technology image/svg+xml Author
  • Tung Chun National Yunlin University of Science and Technology image/svg+xml Author
  • Afsana Khursheed National Yunlin University of Science and Technology image/svg+xml Author

Keywords:

Artificial intelligence, Decision-making models, Early warning systems, Frank aggregation operators, Natural disaster management

Abstract

The selection or assessment of the artificial intelligence-based early warning systems for natural disaster management is very valuable and critical because the selection of the right system directly affects how effectively, quickly, and accurately disasters can be communicated and detected to at-risk communities. The main theme of this manuscript is to develop the novel system of multi-polar fuzzy rough linguistic sets and also describe their valuable operational laws. Further, this study also concentrates on the valuation of the multi-polar fuzzy rough linguistic weighted aggregated sum product assessment models based on Frank norms. Therefore, the averaging operator and geometric operator based on Frank operational laws are also developed for the construction of the above models. Moreover, this study also illustrates some numerical examples for the evaluation of the best and worst decisions among the collection of the considered data. Ultimately, choosing an appropriate artificial intelligence-based early warning system helps reduce loss of life, enhance overall disaster preparedness and resilience, and minimize economic damage. Finally, we compare the ranking values of the proposed information with the ranking values of the existing systems to describe the validity of the invented approaches.

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Author Biographies

  • Hamza Zafar, National Yunlin University of Science and Technology

    Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002, Taiwan, R.O.C.

  • Zeeshan Ali, National Yunlin University of Science and Technology

    Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002, Taiwan, R.O.C.

  • Muhammad Asif, National Yunlin University of Science and Technology

    Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002, Taiwan, R.O.C.

  • Tung Chun, National Yunlin University of Science and Technology

    Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002, Taiwan, R.O.C.

  • Afsana Khursheed, National Yunlin University of Science and Technology

    Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002, Taiwan, R.O.C.

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Additional Files

Published

26-01-2026

Issue

Vol. 1, No. 1 (2025-2026)

Section

Articles

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Copyright (c) 2026 Hamza Zafar, Zeeshan Ali, Muhammad Asif, Tung Chun, Afsana Khursheed (Author)

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How to Cite

Using the MPFRL-WASPAS Model for the Selection of AI-Based Early Warning Systems Based on Natural Disaster Management and Frank Aggregation Operators. (2026). Virosa Journal of AI in Science and Healthcare, 1(1), 1-14. https://virosapub.com/index.php/vjash/article/view/11

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