Abstract: In order to effectively estimate the variation trend of slope deformation, we employed regression analysis and wavelet transform to decompose the trend term and error term of slope deformation. In subsequence we selected data of optimal decomposition and predicted series of trend term and error term by using BP and RBF neural network. Then, we obtained the forecast results of single term and analyzed the forecast results of fixed weight combination and non-fixed weight combination. Results showed that the results of decomposing trend term and error term by different methods are different.Among the methods, polynomial regression with power of six. Fourier regression with power of five and seven and wavelet transform of sym2 have better results. Moreover, partial prediction is prior to conventional prediction of single term, which verifies the effectiveness of partial prediction in the present research. According to combinatorial forecasting results, fixed weight and non-fixed weight both obviously improved prediction accuracy, and the prediction accuracy of the latter is better than that of the former.

Key words: slope deformation, regression analysis, wavelet transform, combination forecasting, trend term