Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area
In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement predictio...
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MDPI AG
2020-06-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/7/1860 |
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| author | Liguo Zhang Xinquan Chen Yonggang Zhang Fuwei Wu Fei Chen Weiting Wang Fei Guo |
| author_facet | Liguo Zhang Xinquan Chen Yonggang Zhang Fuwei Wu Fei Chen Weiting Wang Fei Guo |
| author_sort | Liguo Zhang |
| collection | DOAJ |
| description | In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy. |
| first_indexed | 2024-03-10T18:49:56Z |
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| id | doaj.art-fb42c3bbe4694349b272371570f408fa |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T18:49:56Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-fb42c3bbe4694349b272371570f408fa2023-11-20T05:14:54ZengMDPI AGWater2073-44412020-06-01127186010.3390/w12071860Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir AreaLiguo Zhang0Xinquan Chen1Yonggang Zhang2Fuwei Wu3Fei Chen4Weiting Wang5Fei Guo6College of Mines, Liaoning Technical University, Fuxin 123000, ChinaXiamen Anneng Construction Co., Ltd., Xiamen 361000, ChinaKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education, and Department of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Structural Engineering, Tongji University, Shanghai 200092, ChinaThe Faculty of Engineering, The University of Sydney, Sydney NSW 2006, AustraliaShanghai People’s Procuratorate of Huxi District, Shanghai 200092, ChinaKey Laboratory of Disaster Prevention and Mitigation of Hubei Province, China Three Gorges University, Yichang 443002, ChinaIn order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.https://www.mdpi.com/2073-4441/12/7/1860landslide displacement predictiongray wolf optimization algorithmextreme learning machineGWO-ELM modelthe Three Gorges Reservoir area |
| spellingShingle | Liguo Zhang Xinquan Chen Yonggang Zhang Fuwei Wu Fei Chen Weiting Wang Fei Guo Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area Water landslide displacement prediction gray wolf optimization algorithm extreme learning machine GWO-ELM model the Three Gorges Reservoir area |
| title | Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area |
| title_full | Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area |
| title_fullStr | Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area |
| title_full_unstemmed | Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area |
| title_short | Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area |
| title_sort | application of gwo elm model to prediction of caojiatuo landslide displacement in the three gorge reservoir area |
| topic | landslide displacement prediction gray wolf optimization algorithm extreme learning machine GWO-ELM model the Three Gorges Reservoir area |
| url | https://www.mdpi.com/2073-4441/12/7/1860 |
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