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The Three Gorges Reservoir area, one of the most landslide-prone areas in China, is characterized by widely distributed deep-seated landslides exhibiting creep deformation due to rainfall and reservoir fluctuation. Thresholds, which are a key component for a reliable landslide early warning system, are still lacking for the prediction of movements of deep-seated reservoir landslides with creep deformation because information about reservoir fluctuation indicators is lacking, uncertainty is ignored, and binary output is provided. The risk threshold, defined as the tolerance criteria for risks that will lead to action, is an effective measure of the degree of uncertainty. In the present study, a hybrid approach utilizing kernel density estimation, a copula function, and the value at risk is proposed for the estimation of the risk threshold for the Baishuihe Landslide, a typical deep-seated landslide in the Three Gorges Reservoir area. Historical observations over approximately 15 years including rainfall, reservoir fluctuation, and landslide velocity were used to extract the risk threshold. A three-level risk threshold describing the minimum magnitudes of rainfall and reservoir fluctuation for changing the landslide movement state under three confidence levels was developed. A three-level risk response procedure, including risk responses in yellow alert, orange alert, and red alert, is proposed for risk management. Given its successful application, the present approach can be used to estimate the risk threshold for deep-seated landslides. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]