Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network
Source abstract
Abstract
In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Hydrological Forecasting Using AI · Dam Engineering and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
Funding
National Key R&D Program of China
References (32)
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