Ensemble learning-based classification models for slope stability analysis

Abstract

In this study, ensemble learning was applied to develop a classification model capable of accurately estimating slope stability. Two prominent ensemble techniques—parallel learning and sequential learning—were applied to implement the ensemble classifiers. Additionally, for comparison, eight versatile machine learning algorithms were utilized to formulate the single-learning classification models. These classification models were trained and evaluated on the well-established global database of slope documented from 1930 to 2005. The performance of these classification models was measured by considering the F1 score, accuracy, receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Furthermore, K-fold cross-validation was employed to fairly assess the generalization capacity of these models. The obtained results demonstrated the advantage of ensemble classifiers over single-learning classification models. When ensemble learning was used instead of the single learning, the average F1 score, accuracy, and AUC of the models increased by 2.17%, 1.66%, and 6.27%, respectively. In particular, the ensemble classifiers with sequential learning exhibited better performance than those with parallel learning. The ensemble classifiers on the extreme gradient boosting (XGB-CM) framework clearly provided the best performance on the test set, with the highest F1 score, accuracy, and AUC of 0.914, 0.903, and 0.95, respectively. The excellent performance on the spatially well-distributed database along with its capacity to distribute computing indicates the significant potential applicability of the presented ensemble classifiers, particularly the XGB-CM, for landslide risk assessment and management on a global scale.