The Application of Machine Learning Coupled Models and Optimization Algorithms in the Field of Landslide Displacement Prediction

Accurate landslide displacement prediction is an essential component of landslide early warning systems. The Bazimen landslide in the Three Gorges reservoir area was taken as a case study. In recent years, the outstanding performance of dynamic and static models has attracted the attention of many scholars. Static machine learning algorithm—support vector regression (SVR) and dy-namic machine learning algorithm—long short-term memory neural network (LSTM) were used to predict landslide displacement in this study. At first, to predict landslide displacement accu-rately, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Second, by introducing the K-Nearest Neighbor (KNN), the forecast results of SVR and LSTM models were classified with greater accuracy based on the se-lection of input factors. Finally, we proposed a static-dynamic ensemble landslide displacement predic-tion model optimized with KNN to address the dynamic characteristics of landslide evo-lution and the shortcomings of traditional static prediction models. Through the out-put of the KNN model, the results of the static-dynamic coupling model are updated to get the KNN-optimized SVR-LSTM landslide displacement prediction model. Compared with traditional landslide displacement prediction models that output prediction values, this study optimized the prediction results through a classification algorithm. The results show that the LSTM models show better performance than the SVR models: For monitor-ing point ZG111, the values of RMSE and MAPE with the SVR model were 30.71 mm and 2.15%, while the accuracy factors of the LSTM model were 24.73 mm and 1.87% and the values of RMSE and MAPE with the Ensemble model were 23.11 mm and 1.68%. It can be seen that the ensemble model integrates the advantages of static (SVR) and dynamic (LSTM) models, and its prediction performance is better than that of the single SVR model and the LSTM model. This study provides an integrated view of the landslide displace-ment pre-diction model, which can provide a reference for predicting geological disasters in the Three Gorges reservoir area.