Dam Deformation Prediction Based on Grey Least Square
Support Vector Machines
1 College of Geomatics and Geoinformation, Guilin University of Technology, 12 Jiangan Road, Guilin 541004, China
2 Guangxi Key Laboratory of Spatial Information and Geomatics, 12 Jiangan Road, Guilin 541004, China
Abstract A new algorithm based on gray least squares support vector machine for dam deformation prediction is presented. First, the algorithm of the original dam sequence is summed to weaken the impact of the random disturbance factors sequence and enhance the data regularity. Second, the least squares support vector machine model is established. The grid search method is used to select the optimal parameters. This method makes full use of least squares support vector machine generalization ability| the nonlinear fitting of good quality characteristics avoids defects of the theoretical methods and gray models. The calculation result is analyzed and compared with GM (1, 1) and a single least squares support vector machine. The results show that the new algorithm can guarantee the optimum value of the local forecasts and better overall prediction accuracy in dam deformation. It is feasible to apply the model in short-term forecasts.
REN Chao,LIANG Yueji,PANG Guangfeng et al. Dam Deformation Prediction Based on Grey Least Square
Support Vector Machines[J]. jgg, 2015, 35(4): 608-612.
REN Chao,LIANG Yueji,PANG Guangfeng et al. Dam Deformation Prediction Based on Grey Least Square
Support Vector Machines[J]. jgg, 2015, 35(4): 608-612.
