Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China

Abstract

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models.

Software Defect Prediction Using an Intelligent Ensemble-Based Model

“…Parallel to classification, Ensemble Modeling (EM) has also emerged as a promising technique combining the pre-dictions from multiple ML models to improve overall performance [15]. Ensemble methods, such as bagging, boosting, stacking, and random forest, contribute to the field by mitigating inherent challenges [15].…”

“…Parallel to classification, Ensemble Modeling (EM) has also emerged as a promising technique combining the pre-dictions from multiple ML models to improve overall performance [15]. Ensemble methods, such as bagging, boosting, stacking, and random forest, contribute to the field by mitigating inherent challenges [15]. By aggregating the predictions of multiple base classifiers, ensemble techniques reduce the risks of overfitting, underfitting, and biases that can affect individual classifiers.…”