Next Article in Journal
Effects of Mixed Allelochemicals on the Growth of Microcystis aeruginosa, Microcystin Production, Extracellular Polymeric Substances, and Water Quality
Previous Article in Journal
Correction: Johnes, P.J.; et al. Determining the Impact of Riparian Wetlands on Nutrient Cycling, Storage and Export in Permeable Agricultural Catchments. Water 2020, 12, 167
Open AccessArticle

Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area

1
College of Mines, Liaoning Technical University, Fuxin 123000, China
2
Lijiang Anda Civil Blasting Service Co., Ltd., Lijiang 674100, China
3
Xiamen Anneng Construction Co., Ltd., Xiamen 361000, China
4
Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, and Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China
5
Department of Structural Engineering, Tongji University, Shanghai 200092, China
6
The Faculty of Engineering, The University of Sydney, Sydney NSW 2006, Australia
7
Shanghai People’s Procuratorate of Huxi District, Shanghai 200092, China
8
Key Laboratory of Disaster Prevention and Mitigation of Hubei Province, China Three Gorges University, Yichang 443002, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(7), 1860; https://doi.org/10.3390/w12071860
Received: 28 May 2020 / Revised: 15 June 2020 / Accepted: 17 June 2020 / Published: 29 June 2020
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: landslide displacement prediction; gray wolf optimization algorithm; extreme learning machine; GWO-ELM model; the Three Gorges Reservoir area landslide displacement prediction; gray wolf optimization algorithm; extreme learning machine; GWO-ELM model; the Three Gorges Reservoir area
Show Figures

Figure 1

MDPI and ACS Style

Zhang, L.; Chen, X.; Zhang, Y.; Wu, F.; Chen, F.; Wang, W.; Guo, F. Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area. Water 2020, 12, 1860.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop