New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping
Romulus Costache, Roxana Ţîncu, Ismail Elkhrachy, Quoc Bao Pham, Mihnea Cristian Popa, Daniel Constantin Diaconu, Mohammadtaghi Avand, Iulia Costache, Alireza Arabameri, Dieu Tien Bui
Abstract
High-accuracy flood susceptibility maps play a crucial role in flood vulnerability assessment and risk mitigation. This study assesses the potential application of three new ensemble models, which are integrations of the adaptive neuro-fuzzy inference system (ANFIS), analytic hierarchy process (AHP), certainty factor (CF) and weight of evidence (WoE). The experimental area is the Trotuș River basin in Romania. The database for the present research consisted of 12 flood-related factors and 172 flood locations. The quality of the models was evaluated using root mean square error (RMSE) values and the ROC curve (AUC). The results showed that the ANFIS-CF model and the ANFIS-WOE model have a high prediction capacity (accuracy > 91.6%). Therefore, we concluded that ANFIS-CF and ANFIS-WoE are two new tools that should be considered for future studies related to flood susceptibility modelling.