Abstract
Predicting landslide displacement is of great significance in geotechnical engineering. An iteration-based combined prediction method was proposed for predicting the landslide displacement in this paper. Firstly, the landslide displacement was predicted by 10 latest multivariable grey models, and then the final landslide displacement prediction value was obtained through an iteration-based combined strategy. Concurrently, the reliability of the quadratic programming-based combined prediction method (QPCPM) and the iteration-based combined prediction method (ICPM) was rigorously proved in this paper. In addition, the inapplicability conditions of the optimal weight-based combined prediction method (OWCPM) were pointed out. ICPM could be regarded as a simplified version of QPCPM. The Bazimen and Baishuihe landslides in the Three Gorges Reservoir area of China were used as numerical examples to elaborate the performance of ICPM. This paper also demonstrated the reliability of ICPM by considering the effects of rainfall and reservoir water level on landslide displacement. Overall, ICPM features in simple and easy calculation and has rosy engineering application prospects.
Highlights
In order to consider the instability of landslide displacement, a prediction method based on modified ensemble empirical mode decomposition and extreme learning machine was proposed [8]. e method showed promising results in predicting the landslide displacement of the Baishuihe landslide in the ree Gorges Reservoir area of China
A landslide displacement prediction method based on a multivariate chaotic extreme learning machine was developed for the sake of considering the impact of rainfall and groundwater on the displacement of the landslide [9]. ese methods have successfully predicted the displacement of the Bazimen and Baishuihe landslides in the ree Gorges Reservoir area of China
Is paper developed an iteration-based combined prediction method (ICPM) for landslide displacement prediction based on the above discussions. e feasibility and advantages of this approach have been demonstrated with rigorous mathematical formulas. e structure of this paper is as follows: in Section 2, ICPM developed in this paper was introduced; in Section 3, the Bazimen landslide and the Baishuihe landslide in the ree Gorges Reservoir area of China were used as numerical examples to present the effectiveness of the proposed method in this paper; the conclusions were drawn
Summary
Ese methods have successfully predicted the displacement of the Bazimen and Baishuihe landslides in the ree Gorges Reservoir area of China. A variety of landslide displacement prediction methods based on mathematical models have been proposed. Mathematical Problems in Engineering neuro-fuzzy inference system [11] and the novel kernel extreme learning machine [12] can be applied to landslide displacement. These prediction methods could achieve great results on specific issues. Provided that people use different methods to predict landslide displacements simultaneously, they tend to trust the predicted values with the smallest simulated error. Is paper developed an iteration-based combined prediction method (ICPM) for landslide displacement prediction based on the above discussions. e feasibility and advantages of this approach have been demonstrated with rigorous mathematical formulas. e structure of this paper is as follows: in Section 2, ICPM developed in this paper was introduced; in Section 3, the Bazimen landslide and the Baishuihe landslide in the ree Gorges Reservoir area of China were used as numerical examples to present the effectiveness of the proposed method in this paper; the conclusions were drawn
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
