A Hybrid Multicriteria Index for Assessing Documentary-Methodological Robustness in Flood Mapping: Integrating Documentary Evidence, Entropy-Based Weighting, Remote Sensing, DEMs, Hydrological Data, and Statistical Validation

Flood dynamics can be represented using a growing diversity of remote-sensing sources, digital elevation models (DEMs), hydrological/hydraulic models, and statistical validation techniques. However, no unified evidence-based framework is currently available for systematically comparing the documentary-methodological support of these alternatives. This study proposes a hybrid multicriteria index for assessing the documentary-methodological robustness of flood-mapping methodologies integrating remote sensing, DEMs, hydrological/hydraulic information, and statistical validation methods. Here, documentary-methodological robustness refers to the recurrence, traceability, structural support, and reporting consistency of methodological alternatives within the reviewed corpus; it does not represent technical accuracy, predictive performance, local suitability, or universal methodological superiority. A global documentary review comprising 173 case-study records was organized into six thematic dimensions: optical imagery, SAR imagery, image fusion, DEM information, hydrological/hydraulic information, and statistical validation. Variables and categories were normalized on a 0–1 scale and integrated into thematic base indices using a hybrid weighting strategy that combines author-defined methodological relevance with entropy-derived objective weights. A documentary coverage adjustment was incorporated to account for unequal representation among thematic dimensions. The results showed stronger documentary-methodological support for optical imagery and hydrological/hydraulic information, particularly for Landsat, NDWI, change detection, discharge data, water levels, and hydrodynamic modelling. SAR, image fusion, DEM, and statistical validation dimensions showed comparatively lower but still relevant documentary support. Sensitivity analysis across five λ scenarios showed that five of the six representative methodological combinations retained their robustness class, whereas only the IRIH combination shifted from high to very high robustness. The proposed framework transforms a descriptive review into a quantitative and replicable decision-support instrument for screening methodological alternatives according to their documentary-methodological support. Independent empirical validation remains necessary before selecting a methodology for operational application.