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1- University of Mazandaran
Abstract: (3 Views)
Abstract
Background and Objective: Flooding poses a significant threat to infrastructure, particularly in regions with complex hydrological and topographical characteristics. The Babol county, characterized by its diverse elevation and proximity to riverine systems, is susceptible to flood-induced damage to its transportation and engineering structures. This study aimed to investigate the spatial patterns and density of flood damage across various infrastructure types (roads, bridges, and technical structures) within the rural districts of Babol county. Understanding these spatial distributions is crucial for developing targeted risk assessment and mitigation strategies, prioritizing emergency response, and informing future infrastructure planning.
Method: A spatial analytical framework was employed to examine the distribution and clustering of flood‑induced infrastructure damage. Kernel Density Estimation (KDE) was applied to generate continuous density surfaces and identify areas with high concentrations of damage to roads, bridges, and technical structures. To statistically assess clustering patterns, Hot Spot Analysis using the Getis‑Ord Gi* statistic was conducted, allowing identification of significant hot and cold spots based on z‑scores and p‑values. The combined application of KDE and Getis‑Ord-Gi* enabled both visualization and statistical validation of spatial concentration patterns within the study area.
Findings: The spatial analyses revealed pronounced and non‑random clustering patterns in flood‑related infrastructure damage across the rural districs of Babol county. Kernel Density Estimation (KDE) showed that damage intensity was highly concentrated in specific rural districts. The highest density of road damage occurred in Khosh Rud (36.3 per km²), followed by Sajarud (18.4 per km²), indicating strong localized accumulation of damage events. For bridge damage, Khosh Rud, Sajarud, and Drazkola recorded the greatest densities (0.99 per km²). Similarly, the density of damage to technical structures was highest in Drazkola and Sajarud (0.53 per km²), with notable secondary concentrations in Khosh Rud and Babol Kenar (0.32 per km²).
Hot Spot Analysis using the Getis‑Ord Gi* statistic confirmed these spatial patterns by identifying statistically significant high‑value clusters. Khosh Rud and Sajarud consistently emerged as 99% and 95% confidence hot spots across multiple infrastructure categories, indicating that the intensity of observed damage in these districts is significantly higher than expected under a random spatial process. In contrast, several rural districts in the northern and southern parts of the county were identified as cold spots, exhibiting significantly lower levels of damage.
Conclusion: The spatial analysis of flood damage density highlights specific rural districts (notably Khosh Rud and Sajarud) as critical vulnerability hotspots for infrastructure in Babol County. The consistent high damage concentration across roads, bridges, and technical structures in these areas underscores the need for prioritized interventions. These findings provide a crucial foundation for developing targeted flood risk management and mitigation plans. Focusing resources on these identified hotspots for emergency response, retrofitting, and resilient infrastructure design can significantly enhance the overall safety and functionality of the region’s infrastructure networks against future flood events.
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