FLOOD SUSCEPTIBILITY ANALYSIS USING FREELY AVAILABLE DATA, GIS, AND FREQUENCY RATIO MODEL FOR NAGPUR, INDIA

Nagpur, the geographical center of India, has witnessed flooding consistently for the past ten years due to urbanization and climate change. Cities like Nagpur, which are newly prone to disasters, need to identify flood-prone areas for planning city development and mitigation measures. Developing countries lack data to generate susceptibility maps, thus remote sensing (RS) and GIS have been used in this study. Ten flood-causing parameters namely altitude, slope, topographic wetness index (TWI), landuse/landcover (LULC), soil texture, rainfall, surface runoff, distance from river, lithology and landforms, have been mapped for Nagpur on ArcGIS10.8 using freely available data. The frequency ratio (FR) model has been adopted, with 70% of flood locations used for machine learning and the remaining 30% for validation. Parameters of surface runoff (FR up to 4.30), landform (FR up to 3.58), lithology (FR up to 2.36), and high rainfall (FR up to 1.82) have shown a maximum positive relationship with the flood-prone areas. The results having 80.90% accuracy and validation of 81.58%. proved that the selected parameters can be robustly adopted for mapping flood susceptible areas.