International Journal of Scientific Research and Engineering Development

Flooding is among the most deadliest natural disasters faced by humanity aside from being a significant contributor to vast economic losses, destroyed infrastructure, and even life loss. Based on the need to overcome such disastrous situations, have well planned cities, and be able to identify possible risks, the need to have highly accurate flood susceptibility, as well as risk maps, had be addressed. Within the current study, the integration of the features of the terrain, together with the SVM classification approach, is introduced by the authors with the intention of ensuring a high flood susceptibility, as well as risk assessment, evaluation model. Floods are among the most destructive natural hazards, making the spatial identification of high-risk areas essential for effective land-use planning and disaster management. This study develops a flood susceptibility and risk map by integrating topographical indicators with Support Vector Machine (SVM) classification in a GIS environment. Key terrain-based predictors, including slope, elevation, curvature, drainage density, flow accumulation, and topographic wetness index, are derived from a digital elevation model and combined with historical flood inventory data to train and validate the SVM model. The resulting susceptibility map is classified into distinct hazard zones and further combined with exposure layers such as population density and land use to delineate composite flood risk levels. Model performance is evaluated using receiver operating characteristic (ROC) curves and related accuracy metrics, demonstrating that SVM effectively discriminates between flooded and non-flooded areas. The generated maps provide a decision-support tool for prioritizing mitigation measures, guiding future development, and improving local flood risk management strategies. This current study makes use of the combination of the various features related to the terrain, which are the elevation, slope, aspect, curvatures, and the drainage density, with the aim of predicting regions which might be at risk of flooding. SVM is a highly effective algorithm related to classification techniques, which is the only algorithm used in classifying the regions into the respective regions of the high, moderate, and low flood risk regions. In addition to risk assessment, evaluation model. The present research takes advantage of the integration of the diverse characteristics of the terrain, namely elevation, slope, aspect, curvature, and drainage density, to predict the areas possibly being flooded. SVM is a powerful algorithm associated with classification methods, which is the very algorithm used for dividing the areas into high, moderate, and low flood risk areas.

P.Anuradha, Sairam Lakshman, K.Santhi Swaroop, K.Ravi Kiran, A. Phani,"Flood Susceptibility and Risk Mapping Using Topographical Indicators and Support Vector Machine Classification" International Journal of Scientific Research and Engineering Development, V9(2): Page(986-990) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.