Floods are among the most frequent highly disastrous hazards affecting life, property, and the environment worldwide. While various models are available to predict flood susceptibility, no model is accurate enough to be used for all flood-prone areas. Model development using different algorithms is a continuous process to improve the prediction accuracy of flood susceptibility. In the study, we used the Radial Basis Function and Fisher’s linear discriminant function to develop a flood susceptibility map for a case study of Quang Binh Province. The model development used ten variables (elevation, slope, curvature, river density, distance from river, geomorphology, land use, flow accumulation, flow direction, and rainfall). For model training and validation, input data was split into a 70:30 ratio according to flood locations. Statistical indexes were used to evaluate model performance such as Receiver Operating Characteristic, the Area Under the ROC Curve, Root Mean Square Error, Accuracy, Sensitivity, Specificity, and Kappa index. Results indicated that the radial basis function classifier model had better performance in predicting flood susceptible areas based on the statistical measures (PPV = 92.00%, NPV = 87.00%, SST = 87.62%, SPF = 91.58%, ACC = 89.50%, Kappa = 0.790, MAE = 0.204, RMSE = 0.292 and AUC = 0.957. Therefore, the radial basis function classifier algorithm model is appropriate for predicting flood susceptibility in Quang Binh Province.
Performance of Robust Linear Classifier with Multivariate Binary Variables
