Slope displacement prediction based on morphological filtering

Combining mathematical morphology (MM), nonparametric and nonlinear model, a novel approach for predicting slope displacement was developed to improve the prediction accuracy. A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling. Whereafter, functional-coefficient auto regressive (FAR) models were established for the random subsequences. Meanwhile, the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm. Finally, extrapolation results obtained were superposed to get the ultimate prediction result. Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms. Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm, respectively, which means that the prediction accuracy are improved significantly.