Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India

Abstract

Flood is one of the most damaging catastrophic natural hazards affecting human lives in India. So, flood susceptibility mapping is essential for urban hydrology management. In this study, a Geographical Information System based-bivariate statistical analysis namely frequency ratio (FR) model was used to assess flood susceptibility of the middle and lower catchments of Subarnarekha River. The flood inventory map was made using field surveys and formal reports in the study area. In general, 32 flood locations (70%) that were inundated in June, 2008 was used for statistical analysis as training dataset, while the remaining 30% (14 flood locations) flooded areas were applied to validate the developed model. Eight flood conditioning factors namely elevation, slope, topographical wetness index, geomorphology, soil type, drainage, rainfall, and LULC (land use/land cover) were considered in this study. All these variables were resampled into 20 × 20 m pixel size. Each variable was classified using the quantile method and the FR probability model was used to evaluate the relationship of each class with flood occurrences. Finally, the flood susceptibility map was prepared and classified into very low, low, moderate, high, and very high susceptibility. Results of the built model was validated with the ground data (30% flood locations) using the area under the curves (AUC). The AUC for success rate was estimated as 84.80%, while the prediction rate was 81.20%. The produced flood susceptibility mapping using FR model could be important for researchers, planners, and local governments in order to flood mitigation strategies.