Landslide Susceptibility Assessment Based on Ensemble Learning Modeling

A single machine learning model is often difficult to meet the needs of landslide vulnerability mapping, in order to improve the accuracy of landslide vulnerability assessment. In this paper, a method of machine learning model combination optimization based on integrated strategy is proposed, twelve townships in the west of Yunyang County, Chongqing were taken as an example. First, based on 366 landslide data and 9 index factors such as elevation and slope, the susceptibility evaluation index system was constructed. Then used the three algorithms of ensemble learning, bagging, boosting and stacking, to build combined models based on Decision Tree Mode (DT), Logic Regression (LR) and Bayesian Network (BN).The combined models used Particle Swarm Optimization (PSO), Bayesian Optimization (BO) for super parameter optimization and K-Nearest Neighbor (KNN) was used for model recombination. Finally, ROC curve and statistical analysis were used to calculate the accuracy of each integrated learning model. The research results show that compared with the basic classifier models, the accuracy of the three types of integrated learning models was improved. the DT-LR-BN model increased by 3.5%-12.8%, the RF model increased by 8%; the BO-XGBoost model increased by 13.1%; the KNN-stacking model increased by 7.4%-17%, and the AUC value of BO-XGBoost model was the highest at 0.811. Integrated learning can effectively improve the performance of machine learning models, improve the accuracy of landslide susceptibility mapping, and provide a new idea and method for the combination optimization between machine learning models.