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Slope displacement prediction using sequential intelligent computing algorithms
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DOI：10.1016/j.measurement.2018.10.094.png)
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Slope displacement prediction plays a critical role in the early warning system for landslide and greatly helps prevent property damage and loss of human lives. Given the non-stationary and complex characteristic of the slope deformation, this paper proposes a slope displacement prediction model and an early warming framework based on a set of sequential intelligent computing algorithms that can take advantages of Rough Set theory (RS), Kernel principal component analysis (KPCA), quantum particle swarm optimization (QPSO), least square support vector machine (LSSVM), and Markov chain (MC). Firstly, based on the analysis results of field monitoring data, the environmental parameters affecting the landslide displacement are used as the initial input variables and discretized to remove the effects of dimension and magnitude. After this process, RS is utilized to identify the important influence factors in order to eliminate the multi-collinearity and redundancy of the attributes that were selected initially. As the extracted parameters are still high-dimensional, KPCA is employed to fuse them into a comprehensive indicator to further reduce input dimension and computational cost. The nonlinear relation model between the indicator and displacement is established by using LSSVM, with the parameters optimized through QPSO that has much faster and better global search ability. Once the QPSO-LSSVM model is established, MC is integrated to refine the prediction results. Five months continuous field measurements from the real time monitoring system of Tuyang landslide is applied to evaluate the effectiveness of the proposed model. The results demonstrate that the proposed approach achieves higher prediction accuracy, faster convergence, and better generalization compared with existing prevalent models. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Slope displacement prediction
QPSO-LSSVM
RS
KPCA
Markov chain
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IF:
5.6
论文数:
2.0W
被引数:
5.4W
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