Integrating AHP and machine learning for flood hazard mapping: Evaluating land use planning effectiveness in Ha Tinh, Vietnam
DOI:
https://doi.org/10.12775/EQ.2026.047%20Keywords
flood hazard, machine learning, Support Vector Machine, land use planningAbstract
Flooding is among the most destructive natural hazards in Central Vietnam, causing recurrent damage to infrastructure, rural livelihoods, and ecosystems. This study presents an integrated multi-criteria framework coupling the Analytic Hierarchy Process (AHP) with a Support Vector Machine (SVM) algorithm to refine flood hazard zoning and assess spatial conflicts within the 2030 land use master plan of Ha Tinh province. Six conditioning indicators, proximity to stream, slope, elevation, aspect, land use/land cover, and precipitation, were evaluated and weighted through expert consensus. To resolve the spatial fragmentation typical of linear index overlay, an SVM model with a Radial Basis Function (RBF) kernel was trained on contrastive samples derived from the extreme hazard tiers of the baseline AHP map. The machine learning model demonstrated strong spatial consistency and generalization capability relative to the expert-derived baseline, achieving an Overall Accuracy (OA) of 92.5% and a Kappa coefficient of 0.85 on the validation sample set. Rather than acting as an empirical predictive model against historical inundation events, this workflow operates as a self-referencing spatial optimization tool that effectively smooths rigid, linear classification thresholds into continuous susceptibility gradients. The flood risk map indicated that nearly two-thirds of the province is classified as high or very high flood risk, with the “very high risk” zone alone covering 2840.24 km² (47.66%) and the “high risk” zone 1,027.40 km² (17.24%). Spatial overlay with the provincial 2030 land use plan reveals that 4,100.65 km² of planned land intersects high and very high flood hazard zones, including 2,412.12 km² of forest land, 622.83 km² of paddy land, and 613.77 km² of designated residential areas. These findings demonstrate significant spatial exposure in future development zones, providing actionable spatial evidence to integrate non-structural risk zoning and climate adaptation strategies into long-term territorial planning.
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