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<p>The wider accessibility of rainfall data and data-driven analytical approaches has boosted climate regionalization research worldwide, particularly in Tanzania. However, despite substantial research on rainfall trends and machine learning applications in Tanzania, we did not find a study that systematically analyzed the possibility of reclassifying the country’s traditional rainfall climatic zones using data-driven approaches. As a result, this study fills that gap by investigating long-term rainfall variability and determining whether the current climatic zoning remains valid under observed rainfall patterns. The study used a quantitative, comparative research design with 40 years of monthly rainfall data (1980–2020) from the GPCC and CHIRPS satellite datasets. Machine learning techniques were used to reclassify zones using the k-means, hierarchical clustering, and Partitioning Around Medoids (PAM) algorithms. The study also performed cluster validation and zone agreement analysis (data-driven vs. traditional zones) using silhouette scores, chi-square tests, Cramér’s V, Cohen’s Kappa, and the Adjusted Rand Index (ARI). The results show significant interannual rainfall variability among zones, with no statistically significant long-term trends. Agreement analysis, on the other hand, shows that traditional zoning is robust, despite small divergences in some transitional zones. The divergence indicates that 10 regions out of 31(32.25%) require intra-zonal reclassification. This study presents a novel multi-algorithm machine learning framework that validates Tanzania’s rainfall climate zones over 40 years and identifies intra-zonal heterogeneities, therefore necessitating intra-zonal reclassification. Furthermore, the study offers an analytical framework for improving climate zoning in Tanzania and enhances geographic precision in agricultural planning, water resource management, and climate adaptation measures.</p>

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A Data-Driven Machine Learning Clustering of Rainfall Patterns: Is Reclassification of Tanzanian Rainfall Climate Zones Needed?

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Hussein Abubakar Bakiri,
  • Hadija Ramadhani Mbembati

摘要

The wider accessibility of rainfall data and data-driven analytical approaches has boosted climate regionalization research worldwide, particularly in Tanzania. However, despite substantial research on rainfall trends and machine learning applications in Tanzania, we did not find a study that systematically analyzed the possibility of reclassifying the country’s traditional rainfall climatic zones using data-driven approaches. As a result, this study fills that gap by investigating long-term rainfall variability and determining whether the current climatic zoning remains valid under observed rainfall patterns. The study used a quantitative, comparative research design with 40 years of monthly rainfall data (1980–2020) from the GPCC and CHIRPS satellite datasets. Machine learning techniques were used to reclassify zones using the k-means, hierarchical clustering, and Partitioning Around Medoids (PAM) algorithms. The study also performed cluster validation and zone agreement analysis (data-driven vs. traditional zones) using silhouette scores, chi-square tests, Cramér’s V, Cohen’s Kappa, and the Adjusted Rand Index (ARI). The results show significant interannual rainfall variability among zones, with no statistically significant long-term trends. Agreement analysis, on the other hand, shows that traditional zoning is robust, despite small divergences in some transitional zones. The divergence indicates that 10 regions out of 31(32.25%) require intra-zonal reclassification. This study presents a novel multi-algorithm machine learning framework that validates Tanzania’s rainfall climate zones over 40 years and identifies intra-zonal heterogeneities, therefore necessitating intra-zonal reclassification. Furthermore, the study offers an analytical framework for improving climate zoning in Tanzania and enhances geographic precision in agricultural planning, water resource management, and climate adaptation measures.