Multicriteria Delineation and Stratification of Flood Susceptibility Zones in the Ramis River Basin of the Peruvian Andes

In the Ramis River basin of the Peruvian Andes, flood events have become increasingly frequent and intense due to climate variability. However, the basin has limited hydro-meteorological observation records, and its flood generation mechanisms are extremely complex. This situation not only hinders the accurate identification of flood-prone areas, but also limits the effective implementation of flood risk management measures. This study sets three core objectives: to assess flood sensitivity across the basin, identify the dominant factors that influence flood sensitivity, and verify the flood detection performance of multispectral indices. The study adopts two core methods. First, a multi-criteria framework that integrates the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) is used, incorporating seven flood-related environmental factors and one precipitation triggering variable. Second, the performance of four spectral indices—NDVI, NDWI, SAVI, and MSAVI2 is verified through Spearman correlation analysis, Moran’s I index, and the random forest algorithm. The study finds that landform and geology are the core factors controlling flood sensitivity, with weights of 0.35 and 0.23, respectively. Moderately flood-sensitive areas account for the largest share of the basin, reaching 66% and covering 236.54 km2. The flood extent estimated by the spectral indices ranges from 36.73 km2 to 101.87 km2. Among these indices, NDVI has the strongest spatial correlation with flood-prone areas. The random forest model used in this study has an AUC of 0.6935 and an overall accuracy of 63.51%. The analytical framework proposed in this study is applicable to data-scarce Andean River basins, and the combined use of multispectral indices can provide support for flood risk management and decision-making in this region.