Class Imbalance in Machine Learning for Weather and Rainfall Prediction: A PRISMA-Aligned Systematic Review

Paul Joseph Agada, Ahmad Abubakar Yusuf, Joseph Ubah Adah and Mani Kitgwim Chistopher

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 4, 271-283

Abstract: Machine learning is increasingly used for rainfall and weather-event prediction, yet meteorological classification datasets are commonly dominated by non-event observations. This imbalance creates a methodological problem: a classifier may achieve high overall accuracy while failing to identify the rainy, severe, or otherwise consequential class. This PRISMA-aligned systematic review synthesizes evidence on class imbalance in machine-learning-based weather prediction, emphasizing rainfall classification, balancing methods, traditional classifiers, and performance measurement. Eligibility criteria covered English-language studies on data-driven weather or rainfall prediction that reported classifier development, imbalance treatment, or minority-sensitive evaluation. The evidence was screened and organized around four questions: why weather classes become imbalanced, how imbalance affects learning algorithms, what balancing methods can and cannot achieve, and which evaluation measures provide credible evidence of minority-event skill. The literature indicates that classifier superiority is sensitive to class prevalence, validation design, leakage control, decision threshold, and metric choice. Synthetic oversampling can improve recall and F1-score, but it may reduce precision, increase false alarms, distort local structure, or produce optimistic estimates if applied before data splitting. The review proposes a leakage-safe comparative framework in which balancing is restricted to training data and all models are evaluated on one untouched distribution. It concludes that imbalance should be treated as a central property of weather prediction rather than a secondary preprocessing issue.

Keywords: Class imbalance; Machine learning; Performance evaluation; Rainfall prediction; SMOTENC; Weather classification (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i4:id:1753

DOI: 10.32628/IJSRST2613425

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