Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China

Abstract

The objective of this study is to investigate different ensemble learning techniques namely Bagging, Boosting, and Stacking for LSM at the Jinping county, Southwest China. Two well-known machine learning classifiers such as C4.5 decision tree (C4.5) and artificial neural network (ANN) were served as base-learners. A total of five ensemble models, including the Bag-C4.5 model, the Boost-C4.5 model, the Bag-ANN model, the Boost-ANN model, and the Stacking C4.5-ANN model, were constructed by using various ensemble techniques and base-learners. A landslide inventory map and 12 landslide-related factors have been prepared as the spatial database for landslide modeling. The importance of factors was verified using the information gain (IG) method. It turns out that the distance to roads has the greatest contribution to landslide susceptibility assessment. Subsequently, various landslide models were evaluated regarding the goodness of fit, generalization capability, and robustness. The area under the ROC curve (AUC), statistical analysis, and stability index (SI) were used as performance metrics. Evaluation results showed that ensemble learning techniques significantly refined individual landslide models such as the C4.5 (AUC = 0.832) and ANN (AUC = 0.870). In particular, Boosting-based models, e.g., the Boost-C4.5 model (AUC = 0.945) and the Boost-ANN model (AUC = 0.903), gained a higher performance than the Stacking C4.5-ANN model (AUC = 0.900), the Bag-ANN (AUC = 0.892), and the Bag-C4.5 (AUC = 0.878). Additionally, the best modeling robustness was achieved by the Stacking C4.5-ANN method (SI = 1). The results indicate that the Boosting technique has great confidence in strengthening the predictive accuracy for LSM. Also, the Stacking can provide a promising method for stable and improved landslide modeling. Findings from this study may assist to refine the quality of LSM and facilitate risk management for the study area or other similar regions.