A High-Resolution LiDAR–GIS Framework for Riverine Flood Risk Prediction and Prevention Under Extreme Rainfall

Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR–GIS flood susceptibility framework validated against consecutive record-breaking floods in Dangjin City, South Korea (July 2024: 214.6 mm; July 2025: 377.4 mm). Five terrain parameters—elevation, slope, topographic wetness index, flow accumulation, and distance to stream—were integrated into a weighted Flood Susceptibility Index ( FSI = 0.20 ⋅ E ^ + 0.30 ⋅ S ^ + 0.25 ⋅ T ^ + 0.15 ⋅ F ^ + 0.10 ⋅ D ^ ) and classified into four risk strata using K-means clustering ( k = 4), identifying a high-risk zone of 0.3119 km 2 (5.00% of the 6.18 km 2 analysis domain). A Monte Carlo sensitivity analysis ( n = 5000; ±0.10 weight perturbation) confirmed classification robustness (CV = 5.21%, mean Pearson r = 0.992). Static bathtub inundation scenarios ( Δh = 0.5–2.0 m above the 5th-percentile baseline elevation of 13.29 m AMSL) produced footprint expansion from 0.370 to 0.572 km 2 , capturing all nine observed flood inventory points at the 2.0 m threshold, with flow-connectivity analysis confirming that 91.7–93.1% of predicted inundation is hydraulically connected to the D8-derived stream network. Spatial validation yielded a combined IoU of 6.51%, with a progressive increase from 3.33% (2024) to 6.50% (2025) confirming that the FSI correctly tracks flood-extent expansion with increasing rainfall intensity. Relying exclusively on topographic data and standard GIS algorithms, the framework supports scientifically grounded flood risk governance in data-limited municipalities, directly aligned with SDG 11, SDG 13, and Sendai Framework Target B.