Slope displacement prediction based on morphological filtering
来源期刊：中南大学学报(英文版)2013年第6期
论文作者：LI Qi-yue(李启月) XU Jie(许杰) WANG Wei-hua(王卫华) FAN Zuo-peng(范作鹏)
文章页码：1724 - 1730
Key words：slope displacement prediction; parallel-composed morphological filter; functional-coefficient auto regressive; prediction accuracy
Abstract: 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.
LI Qi-yue(李启月), XU Jie(许杰), WANG Wei-hua(王卫华), FAN Zuo-peng(范作鹏)
(School of Resources and Safety Engineering, Central South University, Changsha 410083, China)
Abstract: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.
Key words:slope displacement prediction; parallel-composed morphological filter; functional-coefficient auto regressive; prediction accuracy
