Flood susceptibility mapping using meta-heuristic algorithms

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important management measures. We compute the map Kaiser watershed in Iran using machine learning models such as support vector (SVM), Particle swarm optimization (PSO), genetic algorithm (GA) along with ensembles (PSO-GA SVM-GA). The application of assessment mapping analyzed, future research suggestions presented. model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, cover, normalize differences vegetation index (NDVI), convergence (CI), topographical wetness (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness (TRI), surface texture (TST), geology stream power (SPI) inventory data which later divided by 70% training 30% validated model. output evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, receiver operating curve (ROC). evaluation method shows robust results from (0.839), particle (0.851), (0.874), SVM-GA (0.886), PSO-GA (0.902). Compared have done some methods commonly used this assessment. A high-quality, informative database essential classification types that very helpful improve performances. performance ensemble better than model, yielding high degree accuracy (AUC-0.902%). Our approach, therefore, provides novel studies other watersheds.