Novel ensemble-based machine learning models based on the bagging, boosting and random subspace methods for landslide susceptibility mapping

Indivisual machine learning models show different limitations such as low generalization power for modeling nonlinear phenomena with complex behavior. In recent years, one of the best approaches to this issue is to use ensemble models. The purpose of this paper is to investigate the predictive power and modeling of three novel ensemble models constructed with four machine learning models: Decision Tree (DT), Support Vector Machine