Geospatial Model Optimization for Mapping Social Vulnerability to Natural Disasters Using Fuzzy Geographically Weighted Clustering and Flower Pollination Algorithm

Deden Istiawan; Ratri Wulandari; Windyaning Ustyannie

doi:10.59395/ijadis.v6i3.1452

Authors

Deden Istiawan Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang
Ratri Wulandari Institut Teknologi Statistika Dan Bisnis Muhammadiyah Semarang
Windyaning Ustyannie Universitas Akprind Indonesia

DOI:

https://doi.org/10.59395/ijadis.v6i3.1452

Keywords:

Geospatial, Disaster, Vulnerability, Mitigation, Mapping

Abstract

This study analyzes social vulnerability to natural disasters in Indonesia through a geospatial optimization model integrating Fuzzy Geographically Weighted Clustering (FGWC) with the Flower Pollination Algorithm (FPA). The hybrid FGWC–FPA enhances clustering accuracy by optimizing spatial parameters and addressing the limitations of index-based and non-spatial methods. The model tested two to four clusters, with the optimal configuration producing four distinct vulnerability groups. Cluster 1 (114 districts) exhibits high poverty, weak infrastructure, and low literacy; Cluster 2 (79 districts) reflects demographic pressure and gender-related inequality; Cluster 3 (87 districts) shows low education and poor disaster preparedness; while Cluster 4 (234 districts) represents health- and age-related vulnerability. A comparison with the 2024 Indonesian Disaster Risk Index (IRBI) shows strong spatial consistency, especially in high-risk regions such as Papua, Maluku, and Sulawesi. The FGWC–FPA model provides finer spatial granularity, allowing the identification of region-specific social issues not captured by deterministic index approaches. The findings validate national disaster risk patterns and offer complementary insights for implementing the National Disaster Management Master Plan (RIPB) 2020–2044, supporting regional prioritization, resource allocation, and capacity-building strategies.

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References

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