Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model
Predicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). The least squares support vector machine (LSSVM) mod...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/4/612 |
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| author | Wenjun Jia Tao Wen Decheng Li Wei Guo Zhi Quan Yihui Wang Dexin Huang Mingyi Hu |
| author_facet | Wenjun Jia Tao Wen Decheng Li Wei Guo Zhi Quan Yihui Wang Dexin Huang Mingyi Hu |
| author_sort | Wenjun Jia |
| collection | DOAJ |
| description | Predicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). The least squares support vector machine (LSSVM) model optimized by particle swarm optimization (PSO) is selected to predict the landslide displacement prediction based on rainfall and reservoir water level (RWL). Five parameters, including rainfall over the previous month, rainfall over the previous two months, RWL, change in RWL over the previous month and period displacement over the previous half year, are selected as the input variables. The relationships between the five parameters and the landslide displacement are revealed by grey correlation analysis. The PSO-LSSVM model is used to predict the periodic term displacement (PTD), and the least squares method is applied to predict the trend term displacement (TTD). With the same input variables, the back propagation (BP) model and the PSO-SVM model are also developed for comparative analysis. In the PSO-LSSVM model, the <i>R</i><sup>2</sup> of three monitoring stations is larger than 0.98, and the MAE values and the RMSE values are the smallest among the three models. The outcomes demonstrate that the PSO-LSSVM model has a high accuracy in predicting landslide displacement. |
| first_indexed | 2024-03-11T07:59:36Z |
| format | Article |
| id | doaj.art-4f2bec5be3fd4691bdbd523356f79a67 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T07:59:36Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-4f2bec5be3fd4691bdbd523356f79a672023-11-16T23:51:04ZengMDPI AGWater2073-44412023-02-0115461210.3390/w15040612Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM ModelWenjun Jia0Tao Wen1Decheng Li2Wei Guo3Zhi Quan4Yihui Wang5Dexin Huang6Mingyi Hu7School of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaPredicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). The least squares support vector machine (LSSVM) model optimized by particle swarm optimization (PSO) is selected to predict the landslide displacement prediction based on rainfall and reservoir water level (RWL). Five parameters, including rainfall over the previous month, rainfall over the previous two months, RWL, change in RWL over the previous month and period displacement over the previous half year, are selected as the input variables. The relationships between the five parameters and the landslide displacement are revealed by grey correlation analysis. The PSO-LSSVM model is used to predict the periodic term displacement (PTD), and the least squares method is applied to predict the trend term displacement (TTD). With the same input variables, the back propagation (BP) model and the PSO-SVM model are also developed for comparative analysis. In the PSO-LSSVM model, the <i>R</i><sup>2</sup> of three monitoring stations is larger than 0.98, and the MAE values and the RMSE values are the smallest among the three models. The outcomes demonstrate that the PSO-LSSVM model has a high accuracy in predicting landslide displacement.https://www.mdpi.com/2073-4441/15/4/612landslide displacement predictionShuping landslideTSMPSO-LSSVM model |
| spellingShingle | Wenjun Jia Tao Wen Decheng Li Wei Guo Zhi Quan Yihui Wang Dexin Huang Mingyi Hu Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model Water landslide displacement prediction Shuping landslide TSM PSO-LSSVM model |
| title | Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model |
| title_full | Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model |
| title_fullStr | Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model |
| title_full_unstemmed | Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model |
| title_short | Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model |
| title_sort | landslide displacement prediction of shuping landslide combining pso and lssvm model |
| topic | landslide displacement prediction Shuping landslide TSM PSO-LSSVM model |
| url | https://www.mdpi.com/2073-4441/15/4/612 |
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