Novel Composite Indices for Exploratory Flood Risk Profiling: Integrating Socioeconomic Proxies and Physical Exposure in the Chi River Basin, Thailand

This study develops a composite risk index (CRI) to assess flood vulnerability in Mueang Maha Sarakham District, northeastern Thailand, by integrating four sub-indices into the CRI—the Net Economic Loss Index (NELI), Income Loss Sensitivity Index (ILSI), Adaptation Cost Index (ACI), and Community Economic Resilience Index (CERI)—with the Maladaptive Development Risk Index (MDRI) reported separately as a contextual factor. The CRI was constructed from 63,017 grid points (approximately 100-m sampling resolution) covering approximately 1236 km2, with elevations ranging from 129 to 203 m. Risk classes were defined using a tertile split of the CRI distribution (20th and 80th percentiles), yielding three approximately balanced classes: Low (20.0%, n = 12,604), Moderate (60.0%, n = 37,809), and High (20.0%, n = 12,604). Spatial clustering (k = 4) identified four distinct risk profiles: Low Risk (32.5%), Moderate Risk (33.0%), Resilient (22.6%), and High Risk (11.9%). Out-of-sample testing against the temporally separated 2025 flood occurrence yielded an area under the receiver operating characteristic curve (AUC) of 0.771 for the CRI, with individual indices achieving AUCs of 0.910 (NELI), 0.844 (ILSI), 0.891 (ACI), 0.816 (CERI, after inversion), and 0.838 (MDRI). The association between risk class and flood occurrence was significant (Cramér’s V = 0.2381, p < 0.001), with the High-risk class containing 993 of the 1077 flooded points. Random Forest (RF) models using spatial block cross-validation achieved AUC-ROC (area under the receiver operating characteristic curve) values of 0.999 (Model A: indices only) and 0.999 (Model B: indices + physical), with AUC-PR (area under the precision–recall curve) of 0.295 and 0.766, respectively. SHapley Additive exPlanations (SHAP) analysis identified mndwi_2024 (Modified Normalized Difference Water Index for 2024), DEM (Digital Elevation Model), flood_freq (flood frequency), and NELI as the most influential predictors. Because Flood_2025 captures flood occurrence rather than consequence severity, these AUC values validate the indices’ association with flood exposure, not their validity as measures of flood consequences. Direct validation of consequence-related performance would require household-level data on economic loss, recovery time, and displacement, which were unavailable in this study. The framework provides a transferable approach for data-scarce regions, but its interpretation should be limited to flood-exposure assessment.