Landslide Displacement Prediction Combining LSTM and SVR Algorithms: A Case Study of Shengjibao Landslide From the Three Gorges Reservoir Area

Applied Sciences

Thomas Glade¹  Haoyuan Hong¹  Hongwei Jiang²  Kunlong Yin²  Yuanyao Li³  Chao Zhou⁴ 

[1] ENGAGE—Geomorphic Systems and Risk Research, Department of Geography and Regional Research, University of Vienna, Vienna 1010, Austria;Faculty of Engineering, China University of Geosciences, Wuhan 430074, China;Geological Survey Institute, China University of Geosciences, Wuhan 430074, China;School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China;

关键词: Shengjibao Landslide;    displacement prediction;    Three Gorges Reservoir area;    long short-term memory neural networks;    support vector regression;    ensemble model;   

DOI  :  10.3390/app10217830

来源: DOAJ

【 摘 要 】

Displacement predictions are essential to landslide early warning systems establishment. Most existing prediction methods are focused on finding an individual model that provides a better result. However, the limitation of generalization that is inherent in all models makes it difficult for an individual model to predict different cases accurately. In this study, a novel coupled method was proposed, combining the long short-term memory (LSTM) neural networks and support vector regression (SVR) algorithm with optimal weight. The Shengjibao landslide in the Three Gorges Reservoir area was taken as a case study. At first, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Single-factor models based on LSTM neural networks and SVR algorithms were used to predict the trend terms of displacement, respectively. Multi-factors LSTM and SVR models were used to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for inputs in the models. Additionally, ensemble models based on the SVR algorithm are used to predict the optimal weight to combine the results of the LSTM and SVR models. The results show that the LSTM models display better performance than SVR models; the ensemble model with optimal weight outperforms other models. The prediction accuracy can be further improved by also considering results from multiple models.

【 授权许可】

Unknown