Multi-Hazard Risk Typology and Temporal Trends of Disaster Events in East Nusa Tenggara Province (2008–2025)
DOI:
https://doi.org/10.63581/JoCPES.v6i2.05Keywords:
Disaster, clustering, temporal trend, East Nusa Tenggara, k-meansAbstract
East Nusa Tenggara (NTT) Province faces complex multi-hazard threats from hydrometeorological and geological disasters. This study developed a multi-hazard risk typology for 22 regencies/cities in NTT and examined temporal trends in disaster frequency over 2008–2025 (complete years; partial 2026 data excluded). The dataset comprised 1,158 records, adjusted to 1,128.5 real events using fractional weights across eight disaster types. Clustering was evaluated across three feature schemes and two algorithms (K-Means and Agglomerative Ward), with the number of clusters validated through a consensus of Silhouette, Davies-Bouldin, and Calinski-Harabasz indices while avoiding degenerate solutions. Temporal trends and change points were evaluated using the Mann-Kendall and Pettitt tests, preceded by lag-1 autocorrelation (ACF) screening. The selected hazard-only Agglomerative Ward model (k = 6; Silhouette = 0.2105, DBI = 1.205, CHI = 5.28) categorized regions into six distinct typologies, from low-risk to high-frequency flood/extreme-weather clusters, with Sabu Raijua identified as an impact-severity outlier. Auto correlation checks confirmed the validity of test assumptions. The Mann-Kendall and Pettitt tests consistently revealed no statistically significant monotonic trend or change point (p > 0.05) across the province, dominant disaster types, and all clusters. This meaningful null result demonstrates that disaster occurrences in NTT are driven by sharp inter-annual fluctuations rather than long-term trends, emphasizing the necessity of continuous preparedness and differentiated, typology-based mitigation strategies.
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Data Availability Statement
The disaster event data used in this study were obtained from the Data Informasi Bencana Indonesia (DIBI), a publicly accessible database maintained by the National Disaster Management Authority (Badan Nasional Penanggulangan Bencana, BNPB), Republic of Indonesia, available at https://dibi.bnpb.go.id/superset/dashboard/2/. The processed dataset and analysis code supporting the findings of this study are available from the corresponding author upon reasonable request.
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