Abstract
Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.
Highlights
In the Three Gorges Reservoir region, landslides are the main type of geohazard, and they cause critical harm to individuals and property each year (Du et al, 2013; Yao et al, 2013; Lian et al, 2014; Cao et al, 2016)
Landslide displacement prediction is a major focus of contemporary landslide research
According to time series analysis, the cumulative displacement is decomposed into a trend component displacement representing the trend of landslide deformation in the long-term and a periodic component displacement that represents short-term deformation fluctuations
Summary
In the Three Gorges Reservoir region, landslides are the main type of geohazard, and they cause critical harm to individuals and property each year (Du et al, 2013; Yao et al, 2013; Lian et al, 2014; Cao et al, 2016). The LSSVM model uses the square sum of the least-squares linear system error as the loss function and solves the problem by transforming it into a set of equations, which increases the solution speed and reduces the required calculation resources (Suykens et al, 2002; Lv et al, 2013; Xu and Chen, 2013; Zhang et al, 2013) This method yields good performance in pattern recognition and nonlinear function fitting. Due to the influences of rainfall, reservoir water level and human activities on the monitoring data of landslide displacement, most monitoring data series are incomplete or highly variable These issues introduce uncertainty into the mathematical model and increase the difficulty of prediction. The Shuping landslide, a typical landslide with episodic movement deformation, was taken as an example to validate the GA-LSSVM model with time series analysis
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