Hydroclimatic Regionalization in Indonesia: A Comparative Study of Fuzzy C-Means and Gaussian Mixture Models

Bunga Anovia Putri Ramadhianti, A'yunin Sofro

Abstract


Drought is a climate-related natural hazard that poses significant challenges to environmental sustainability and disaster risk reduction in Indonesia, particularly in the context of achieving Sustainable Development Goal 13 (Climate Action). However, drought-related characteristics vary considerably across regions due to differences in local climatic conditions. This study aims to regionalize drought-related hydroclimatic characteristics across 34 provinces in Indonesia using Fuzzy C-Means (FCM) and Gaussian Mixture Models (GMM). The analysis used climatological data from CHIRPS Daily and ERA5-Land covering the 2015–2025 period. Four indicators were considered, including rainfall, average temperature, number of rainy days, and consecutive dry days (CDD). The optimal number of clusters was determined using the Xie–Beni Index for FCM and the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for GMM, supported by cluster stability assessment based on the Adjusted Rand Index and additional validation using the Gap Statistic. Both methods identified two clusters as the optimal solution, consistently separating a relatively drier group of provinces from a relatively wetter group. Cluster quality was evaluated using the Silhouette Coefficient and Davies–Bouldin Index (DBI). The results showed that FCM provided marginally better clustering performance, with a Silhouette Coefficient of 0.345 and a DBI of 1.076, compared with 0.332 and 1.115, respectively, for GMM, although both methods produced broadly consistent regionalization. The resulting clusters reveal long-term drought-related regional patterns across Indonesia, providing a quantitative basis for identifying provinces with similar hydroclimatic characteristics to support further, more targeted drought-related research.


Keywords


Fuzzy C-Means; Gaussian Mixture Models; hydroclimatic characteristics; regionalization; SDG 13

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References


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DOI: https://doi.org/10.18860/cauchy.v11i2.45896

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