Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

Abstract

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment

“…The classification error E k confirms the correctness of the classification and updates the weight W k + 1 , as shown in Equation (21). The iterative calculation of classification is completed when the error E k is less than the preset threshold.…”

“…Machine learning algorithms enrich the quality and accuracy of generated susceptibility maps. Researchers use and compare various machine learning models on the basis of different data [16][17][18][19], integrate different machine learning models to improve accuracy [20][21][22][23], or develop new algorithms that are based on traditional machine learning models to strengthen landslide prediction results [24][25][26]. These techniques perform better than do classical methods.…”

Embedded FBG Sensor Based Impact Identification of CFRP Using Ensemble Learning

“…The structure of stacking consists of two levels, namely level-0 and level-1. The outputs of multiple base learners (level-0) are combined by the meta-learner (level-1), as shown in Figure 3 [ 42 ]. These machine learning methods from level-0 are commonly used for basic predictors, and the other methods from level-1 are usually for the combination of those base learners.…”

Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping

“…Ensemble learning offers the possibility to further improve the accuracy and reflection of nonlinear relationships between landslide and conditioning factors [17]. Bagging, boosting and stacking are three commonly applied technologies [18][19][20][21][22]. However, few discussions have focused on the integration of TMLM.…”