Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China
Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the c...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-12-01
|
| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://www.nat-hazards-earth-syst-sci.net/17/2181/2017/nhess-17-2181-2017.pdf |
| _version_ | 1818366527915687936 |
|---|---|
| author | T. Wen H. Tang H. Tang Y. Wang Y. Wang C. Lin C. Xiong |
| author_facet | T. Wen H. Tang H. Tang Y. Wang Y. Wang C. Lin C. Xiong |
| author_sort | T. Wen |
| collection | DOAJ |
| description | Predicting landslide displacement is
challenging, but accurate predictions can prevent casualties and economic
losses. Many factors can affect the deformation of a landslide, including the
geological conditions, rainfall and reservoir water level. Time series
analysis was used to decompose the cumulative displacement of landslide into
a trend component and a periodic component. Then the least-squares support
vector machine (LSSVM) model and genetic algorithm (GA) were used to predict
landslide displacement, and we selected a representative landslide with
episodic movement deformation as a case study. The trend component
displacement, which is associated with the geological conditions, was
predicted using a polynomial function, and the periodic component
displacement which is associated with external environmental factors, was
predicted using the GA-LSSVM model. Furthermore, based on a comparison of the
results of the GA-LSSVM model and those of other models, the GA-LSSVM model
was superior to other models in predicting landslide displacement, with the
smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error
(MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of
1.492 % at monitoring station ZG85, while these three values are
87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm,
48.5392 mm and 3.131 % at ZG87. The results of the case study suggest
that the model can provide good consistency between measured displacement and
predicted displacement, and periodic displacement exhibited good agreement
with trends in the major influencing factors. |
| first_indexed | 2024-12-13T22:37:35Z |
| format | Article |
| id | doaj.art-58c1ce48003e4b009118ad8ee61282a4 |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-12-13T22:37:35Z |
| publishDate | 2017-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-58c1ce48003e4b009118ad8ee61282a42022-12-21T23:28:56ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812017-12-01172181219810.5194/nhess-17-2181-2017Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, ChinaT. Wen0H. Tang1H. Tang2Y. Wang3Y. Wang4C. Lin5C. Xiong6Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of ChinaThree Gorges Research Center for Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of ChinaThree Gorges Research Center for Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of ChinaThree Gorges Research Center for Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of ChinaPredicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.https://www.nat-hazards-earth-syst-sci.net/17/2181/2017/nhess-17-2181-2017.pdf |
| spellingShingle | T. Wen H. Tang H. Tang Y. Wang Y. Wang C. Lin C. Xiong Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China Natural Hazards and Earth System Sciences |
| title | Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China |
| title_full | Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China |
| title_fullStr | Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China |
| title_full_unstemmed | Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China |
| title_short | Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China |
| title_sort | landslide displacement prediction using the ga lssvm model and time series analysis a case study of three gorges reservoir china |
| url | https://www.nat-hazards-earth-syst-sci.net/17/2181/2017/nhess-17-2181-2017.pdf |
| work_keys_str_mv | AT twen landslidedisplacementpredictionusingthegalssvmmodelandtimeseriesanalysisacasestudyofthreegorgesreservoirchina AT htang landslidedisplacementpredictionusingthegalssvmmodelandtimeseriesanalysisacasestudyofthreegorgesreservoirchina AT htang landslidedisplacementpredictionusingthegalssvmmodelandtimeseriesanalysisacasestudyofthreegorgesreservoirchina AT ywang landslidedisplacementpredictionusingthegalssvmmodelandtimeseriesanalysisacasestudyofthreegorgesreservoirchina AT ywang landslidedisplacementpredictionusingthegalssvmmodelandtimeseriesanalysisacasestudyofthreegorgesreservoirchina AT clin landslidedisplacementpredictionusingthegalssvmmodelandtimeseriesanalysisacasestudyofthreegorgesreservoirchina AT cxiong landslidedisplacementpredictionusingthegalssvmmodelandtimeseriesanalysisacasestudyofthreegorgesreservoirchina |