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<p>Severe tropical cyclones are powerful weather phenomena that may be destructive to society in many ways, including countless fatalities, deaths, and considerable damage to property, economy, and infrastructure in a short period. This study proposes and assesses a decision support system based on granular computing and rough set theory. This is used to find the granules of key tropical cyclone-related parameters that could help make severe cyclonic storms likely or less likely to happen in the Arabian Sea and the Bay of Bengal in the North Indian Ocean. The results obtained from the decision support system regarding the occurrence of severe tropical cyclones are evaluated using a Z-number-based unique quantification measure, ‘<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(Z_{scs}\)</EquationSource> </InlineEquation>’. This boosts the reliability of the prediction, and a more accurate interpretation of decision information is found in terms of linguistic abstraction for the threshold granules of cyclone-related parameters. The predictions of decision rules are validated and justified using inverse-decision rules derived from rough set theory. This study intends to demonstrate how to integrate decision rules, inverse decision rules, flow graphs, and Z-numbers for assessment, forecasting, and interpretation. Our proposed research framework improves the reliability and accuracy of risk assessment, assisting managers and decision-makers in formulating more effective risk mitigation strategies.</p>

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Analysing and interpreting the risk of the genesis of severe tropical cyclones using Z-number-based granulated rough decision support system

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Debashree Dutta,
  • Sankar K. Pal

摘要

Severe tropical cyclones are powerful weather phenomena that may be destructive to society in many ways, including countless fatalities, deaths, and considerable damage to property, economy, and infrastructure in a short period. This study proposes and assesses a decision support system based on granular computing and rough set theory. This is used to find the granules of key tropical cyclone-related parameters that could help make severe cyclonic storms likely or less likely to happen in the Arabian Sea and the Bay of Bengal in the North Indian Ocean. The results obtained from the decision support system regarding the occurrence of severe tropical cyclones are evaluated using a Z-number-based unique quantification measure, ‘ \(Z_{scs}\) ’. This boosts the reliability of the prediction, and a more accurate interpretation of decision information is found in terms of linguistic abstraction for the threshold granules of cyclone-related parameters. The predictions of decision rules are validated and justified using inverse-decision rules derived from rough set theory. This study intends to demonstrate how to integrate decision rules, inverse decision rules, flow graphs, and Z-numbers for assessment, forecasting, and interpretation. Our proposed research framework improves the reliability and accuracy of risk assessment, assisting managers and decision-makers in formulating more effective risk mitigation strategies.