Spatial Grouping of Tornado-Relevant Wind Regimes Areas in Indonesia to Enhance Disaster Risk Mitigation Capacity - Sela Naren Ardelita; A'yunin Sofro (ZERO: Jurnal Sains, Matematika dan Terapan, 2025)
Tornadoes are major weather hazards in Indonesia, where wind variability is important for assessing disaster risk and supporting energy planning. This study conducts a short-term (one-year) analysis by identify similarities in regional wind speed patterns using a time-series clustering approach, treating monthly average wind speeds in 2024 as proxies for tornado-relevant wind regimes rather than direct tornado occurrence data. Agglomerative hierarchical clustering is integrated with three distance measures-Dynamic Time Warping (DTW), Autocorrelation Function (ACF), and Short Time Series (STS)-and optimized using Brain Storm Optimization (BSO) to determine optimal distance weighting and cluster numbers. The results indicate that DTW provides the best performance, yielding a two-cluster solution with a Silhouette Coefficient of 0.5292. The first cluster exhibits relatively stable wind patterns, while the second shows higher temporal variability. This framework provides a data-driven basis for region-specific wind energy planning and tornado-adaptive infrastructure considerations in Indonesia.