Novel ensemble machine learning models in flood susceptibility mapping

Abstract

The research aims to propose the new ensemble models by combining the machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbour (KNN), boosted regression tree (BRT), and logitboost (LB) with the base classifier adabag (AB) for flood susceptibility mapping (FSM). The proposed models were implemented in the central west coast of India, which is vulnerable to flood events. For flood inventory mapping, a total of 210 flood localities were identified. Twelve effective factors were selected using the boruta algorithm for FSM. The area under the receiver operating characteristics (AUROC) curve and other statistical measures (sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and mean absolute error (MAE)) were employed to estimate and compare the success rate of the approaches. The validation results of the individual models in terms of AUC value were AB (92.74 percent) greater than RF (91.50 percent) greater than BRT (90.75 percent) greater than LB (89.07 percent) greater than NSC (88.97 percent) greater than KNN (83.88 percent), whereas the ensemble models showed that the AB-RF (94 percent) was of the highest prediction efficiency followed by, AB-KNN (93.33 percent), AB-NSC (93.02 percent), AB-LB (92.83 percent), and AB-BRT (92.64 percent). The outcomes of the ensemble models established that the AB is more appropriate to increase the accuracy of different single models. Therefore, this study can be useful for proper planning and management of the study area and flood hazard mapping in alike geographic environment.