Kanglin Cong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Kanglin Cong has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chuang Chen
Nanjing Tech University
1 shared publication - Jianping Yue
College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
1 shared publication - Shaolin Zhu
College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
1 shared publication - Yankai Bian
School of Earth Sciences and Engineering, Hohai University, Nanjing, China
1 shared publication - Yin Xing
School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, China
1 shared publication
Latest Publications
- Open Access
- 24 July 2019
- Published by MDPI AG in Applied Sciences
- Vol. 9 (15) , 2951
Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network
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.
- 24 July 2019
- Published by MDPI AG in Applied Sciences
- Vol. 9 (15) , 2951
Open AccessDynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network
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.