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<p>The climate change forecast (CCF) is a complex decision-making challenge involving various factors like atmosphere, human behavior, and ecological impacts, often containing uncertainties and incomplete information, making accurate modeling challenging. Accordingly, it necessitates a robust mathematical and computational framework that can handle uncertainty and vagueness through similarity measures based rough and multi-argument approximations. The concepts of similarity measures caused by hypersoft sets and hypersoft fuzzy sets are introduced based on an investigation of the current soft fuzzy rough techniques. This research explores the concepts of hypersoft rough set (HSRS) and hypersoft fuzzy rough set (HFRS), providing illustrative examples and results. The existing rough approximations of rough set (RS) have been improved by integrating approximation spaces based on HSRS and HFRS. A new formulation has been introduced to determine the degrees of similarity among alternatives by utilizing approximations of multi-argument parameters within HSRS and HFRS frameworks. To address uncertainties and incompleteness in environmental data, a robust algorithm is developed that employs rough approximations, similarity degrees, and accuracy measures to evaluate suitable strategies. The algorithm is demonstrated through a prototype case study, and the computational complexity, particularly in terms of time, is also assessed for the proposed algorithm.</p>

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An Intelligent Decision-Support System for Climate Change Mitigation Using Similarity Measures of Hypersoft Rough Sets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月21日

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  • Muhammad Abdullah,
  • Khuram Ali Khan,
  • Atiqe Ur Rahman,
  • Michael Kikomba Kahungu

摘要

The climate change forecast (CCF) is a complex decision-making challenge involving various factors like atmosphere, human behavior, and ecological impacts, often containing uncertainties and incomplete information, making accurate modeling challenging. Accordingly, it necessitates a robust mathematical and computational framework that can handle uncertainty and vagueness through similarity measures based rough and multi-argument approximations. The concepts of similarity measures caused by hypersoft sets and hypersoft fuzzy sets are introduced based on an investigation of the current soft fuzzy rough techniques. This research explores the concepts of hypersoft rough set (HSRS) and hypersoft fuzzy rough set (HFRS), providing illustrative examples and results. The existing rough approximations of rough set (RS) have been improved by integrating approximation spaces based on HSRS and HFRS. A new formulation has been introduced to determine the degrees of similarity among alternatives by utilizing approximations of multi-argument parameters within HSRS and HFRS frameworks. To address uncertainties and incompleteness in environmental data, a robust algorithm is developed that employs rough approximations, similarity degrees, and accuracy measures to evaluate suitable strategies. The algorithm is demonstrated through a prototype case study, and the computational complexity, particularly in terms of time, is also assessed for the proposed algorithm.