Abstract
Accurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on predictive confidence. This study applied interval prediction for landslide displacement. Taking the Tanjiahe landslide of the Three Gorges Reservoir Area as an example and considering the impact of seasonal variations in reservoir level and rainfall, the uncertainties associated with landslide displacement prediction were quantified into prediction intervals (PIs) by a bootstrapped least-square support vector machine (LSSVM) method (B-LSSVM). The proposed method consists of three steps: First, the LSSVM and bootstrapping were combined to estimate the true regression means of landslide displacement and the variance with respect to model misspecification uncertainties. Second, a new LSSVM model optimized by a genetic algorithm (GA) was implemented to estimate the noise variance. Finally, the point prediction was derived from the regression means, and the PIs were constructed by combining the regression mean, the model variance, and the noise variance. We applied the proposed method to predict the displacement of four GPS monitoring points of the Tanjiahe landslide, and we comprehensively compared the prediction accuracy and the quality of the constructed PIs with benchmark methods. A simulation and performance comparison showed that the proposed method is a promising technique for providing accurate and reliable prediction results for landslide displacement.
Highlights
Landslides are the most frequent geological hazard in China
To verify the efficacy and superiority of the B-least-square support vector machine (LSSVM) method, multiple methods, including BP, ELM, and LSSVM tuned by the default optimization algorithm of the LS-SVMlab toolbox, were applied for point prediction comparison, and a hybrid model of ANN-based prediction intervals (PIs), namely, B-ELM [16], was used for probabilistic forecast comparison
Since the coverage width-based criterion (CWC) is a comprehensive index balancing the prediction interval coverage probability (PICP) and normalized mean PI width (NMPIW), it can be stated that the B-LSSVM method outperforms the B-ELM model and can generate more high-quality PIs in Tanjiahe landslide displacement prediction
Summary
Landslides are the most frequent geological hazard in China. These events seriously threaten infrastructure and the safety of human life and result in extensive casualties and property losses every year. Data-driven methods require only the available monitoring data and not the physical parameters of the landslide. Cao et al [3], Huang et al [5, 6], and Lian et al [7] applied an ELM-based model for point prediction of landslide displacement, and the performance of proposed models is tested in reservoir landslides of the TGRA. Cai et al [8], Cao et al [3], Miao et al [9], Ren et al [10], and Wen et al [11] proposed SVM-based hybrid models to predict the displacement of reservoir landslides, and the numerical results demonstrate that their hybrid method outperformed artificial neural network-based methods. Most predictions are deterministic (or point) predictions; i.e., they can only provide a crisp displacement value and cannot provide the uncertainty associated with the predictions
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