Mapping potential inundation areas due to riverine floods using ensemble models of credal decision tree with bagging, dagging, decorate, multiboost, and random subspace

Abstract

This study aims at presenting innovative ensemble models for mapping potential inundation areas due to riverine floods in the Najafabad basin in Iran. Altogether, a total of 16 flood causative factors were derived and used as independent variables based on Variance Inflation Factor (VIF) multi-collinearity test. Flood inventory consisted of 154 riverine flood locations, which were used in training and testing phases of modelling procedures. At the first stage, we developed a standalone credal decision tree (CDT) model and then integrated with five algorithms: bagging (BA), dagging (DA), multiboost (MB), decorate (DE), and random subspace (RSS). The validation results in case of the training data confirmed very high accuracy of all six models used, i.e. CDT, BA-CD, DA-CDT, DE-CDT, MB-CDT, and RSS-CDT models recorded the AUC-ROC values of 0.910, 0.944, 0.950, 0.931, 0.992, and 0.956, respectively. With regard to validation data, five ensemble models (BA-CD, DA-CDT, DE-CDT, MB-CDT, and RSS-CDT) recorded AUC-ROC values higher than 0.9 while only the standalone CDT model recorded the AUC-ROC value of 0.844. The best accuracy was recorded by the ensemble MB-CDT model with the AUC-ROC values of 0.992 and 0.941 in case of the training phase and testing phase, respectively. The results presented in this study are useful for flood risk management and, especially during the preliminary phase, where the most susceptible areas that can be inundated by riverine flooding are determined.