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<p>This paper aims to use sustainable computing through modern statistical approaches for drought event classification. By utilizing advanced methodologies such as multivariate analysis, it seeks to investigate further the intricate associations between meteorological variables. The objective of this approach is not only to improve the accuracy and interpretability of the models for predicting drought but also to help understand hidden details on intricate interconnections that regulate drought conditions. In studying this, valuable information can be added concerning the joint distribution of the climatic features that will consequently assist in the enhancement of understanding regarding drought dynamics, which may lead to better drought management and mitigation strategies.</p>

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Sustainable computing using machine learning and copula-based modelling for drought classification from meteorological features

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • Sanjeev Kumar,
  • Karandeep Saluja,
  • Dilkeshwar Pandey,
  • Saurav Chandra,
  • Rahul Silori,
  • Balendu Shekher Giri

摘要

This paper aims to use sustainable computing through modern statistical approaches for drought event classification. By utilizing advanced methodologies such as multivariate analysis, it seeks to investigate further the intricate associations between meteorological variables. The objective of this approach is not only to improve the accuracy and interpretability of the models for predicting drought but also to help understand hidden details on intricate interconnections that regulate drought conditions. In studying this, valuable information can be added concerning the joint distribution of the climatic features that will consequently assist in the enhancement of understanding regarding drought dynamics, which may lead to better drought management and mitigation strategies.