Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model
Landslide displacement prediction is an important part of monitoring and early warning systems. Effective displacement prediction is instrumental in reducing the risk of landslide disasters. This paper proposes a displacement prediction model based on variational mode decomposition and a genetic alg...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/450 |
| _version_ | 1827760999408599040 |
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| author | Wei Guo Qingjia Meng Xi Wang Zhitao Zhang Kai Yang Chenhui Wang |
| author_facet | Wei Guo Qingjia Meng Xi Wang Zhitao Zhang Kai Yang Chenhui Wang |
| author_sort | Wei Guo |
| collection | DOAJ |
| description | Landslide displacement prediction is an important part of monitoring and early warning systems. Effective displacement prediction is instrumental in reducing the risk of landslide disasters. This paper proposes a displacement prediction model based on variational mode decomposition and a genetic algorithm optimization of the Elman neural network (VMD–GA–Elman). First, using VMD, the landslide displacement sequence is decomposed into the three subsequences of the trend term, the periodic term, and the random term. Then, appropriate influencing factors are selected for each of the three subsequences to construct input datasets; the rationality of the selection of the influencing factors is evaluated using the gray correlation analysis method. The GA–Elman model is used to forecast the trend item, periodic item and random item. Finally, the total displacement is obtained by superimposing the three subsequences to verify the performance of the model. A case study of the Shuizhuyuan landslide (China) is presented for the validation of the developed model. The results show that the model in this paper is in good agreement with the actual situation and has good prediction accuracy; it can, therefore, provide a basis for early warning systems for landslide displacement and deformation. |
| first_indexed | 2024-03-11T10:07:44Z |
| format | Article |
| id | doaj.art-4aec9fb4b3f344d0b77362f39fdd5416 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:07:44Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4aec9fb4b3f344d0b77362f39fdd54162023-11-16T14:57:10ZengMDPI AGApplied Sciences2076-34172022-12-0113145010.3390/app13010450Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman ModelWei Guo0Qingjia Meng1Xi Wang2Zhitao Zhang3Kai Yang4Chenhui Wang5Technology Innovation Center for Geological Environment Monitoring, Ministry of Natural Resources, Baoding 071051, ChinaTechnology Innovation Center for Geological Environment Monitoring, Ministry of Natural Resources, Baoding 071051, ChinaTechnology Innovation Center for Geological Environment Monitoring, Ministry of Natural Resources, Baoding 071051, ChinaSchool of Business Administration, Northwest A&F University, Yangling, Xianyang 712100, ChinaTechnology Innovation Center for Geological Environment Monitoring, Ministry of Natural Resources, Baoding 071051, ChinaTechnology Innovation Center for Geological Environment Monitoring, Ministry of Natural Resources, Baoding 071051, ChinaLandslide displacement prediction is an important part of monitoring and early warning systems. Effective displacement prediction is instrumental in reducing the risk of landslide disasters. This paper proposes a displacement prediction model based on variational mode decomposition and a genetic algorithm optimization of the Elman neural network (VMD–GA–Elman). First, using VMD, the landslide displacement sequence is decomposed into the three subsequences of the trend term, the periodic term, and the random term. Then, appropriate influencing factors are selected for each of the three subsequences to construct input datasets; the rationality of the selection of the influencing factors is evaluated using the gray correlation analysis method. The GA–Elman model is used to forecast the trend item, periodic item and random item. Finally, the total displacement is obtained by superimposing the three subsequences to verify the performance of the model. A case study of the Shuizhuyuan landslide (China) is presented for the validation of the developed model. The results show that the model in this paper is in good agreement with the actual situation and has good prediction accuracy; it can, therefore, provide a basis for early warning systems for landslide displacement and deformation.https://www.mdpi.com/2076-3417/13/1/450landslide displacement predictionvariational mode decompositiongenetic algorithmElman neural networkgray relational analysis |
| spellingShingle | Wei Guo Qingjia Meng Xi Wang Zhitao Zhang Kai Yang Chenhui Wang Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model Applied Sciences landslide displacement prediction variational mode decomposition genetic algorithm Elman neural network gray relational analysis |
| title | Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model |
| title_full | Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model |
| title_fullStr | Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model |
| title_full_unstemmed | Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model |
| title_short | Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model |
| title_sort | landslide displacement prediction based on variational mode decomposition and ga elman model |
| topic | landslide displacement prediction variational mode decomposition genetic algorithm Elman neural network gray relational analysis |
| url | https://www.mdpi.com/2076-3417/13/1/450 |
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