Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function


  • Chinh Luu Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi, Vietnam
  • Duc Dam Nguyen University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam
  • Mahdis Amiri Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, 49189-434, Iran
  • Tran Van Phong Institute of Geological Sciences, VAST, Hanoi, Vietnam
  • Quynh Duy Bui Department of Geodesy, National University of Civil Engineering, Hanoi, Vietnam
  • Indra Prakash DDG(R) Geological Survey of India, Gandhinagar 382015, India
  • Binh Thai Pham University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam




Radial Basis Function Classifier, Fisher’s linear discriminant function, Important variables, floods, Quang Binh


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.


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How to Cite

Luu, C. ., Dam Nguyen, D., Amiri, M. ., Tran Van, P., Duy Bui, Q. ., Prakash, I. ., & Thai Pham, B. . (2021). Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function. Vietnam Journal of Earth Sciences. https://doi.org/10.15625/2615-9783/16626