Abstract Floods are among the most devastating natural disasters worldwide, necessitating effective disaster management strategies to mitigate their impacts. This study focuses on the identification of optimal safe assembly areas in the case of urban flash floods using Geographic Information System (GIS) and machine learning techniques. The Bartın River Basin, which has a history of severe flood events, was selected as the study area. A total of 79 micro-basins covering 2,342.87 km 2 were analyzed using 17 parameters related to topography, hydrology, infrastructure, and demography. After normalization, Principal Component Analysis (PCA) was applied to reduce dimensionality from 17 to 2–16 components. Seven clustering algorithms (K-Means, Agglomerative Hierarchical, DBSCAN, MeanShift, Birch, Mini Batch K-Means, and Spectral) were tested, and their performances were compared using the Silhouette Score Index (SSI). Results indicate that the K-Means algorithm with 2 principal components and 3 clusters achieved the best performance (SSI > 0.5), identifying micro-basins 9, 34, and 41 as the most suitable assembly areas. Post-clustering validation revealed that these areas combine low flood risk indicators with high accessibility. More than 85% of the basin’s 206,715 inhabitants can reach a safe assembly point within 30 min (≤ 30 km at an average evacuation speed of 30 km/h). Notably, micro-basin 9 alone provides access for 68.3% of the population within 5–15 km, highlighting its strategic importance. Historical flood data (2020–2024) further confirmed that two of the identified basins are located in zones with fewer past flood events, reinforcing their reliability. The proposed framework bridges theoretical optimization and real-world feasibility, providing actionable insights for disaster planners. Future research will focus on large-scale evacuation simulations and the integration of population flows and shelter capacities to further strengthen operational applicability.
Karakaya et al. (Sun,) studied this question.