Suitability of data preprocessing methods for landslide displacement forecasting

Data preprocessing is an indispensable step for landslide displacement forecasting, which is an effective approach for predicting the deformation and failure behaviors of landslides. However, most studies focus on the construction of displacement forecast models and ignore the influence of data preprocessing on the forecasting results. Data normalization is an important part of data preprocessing; however, the selection of a data normalization method is subjective and arbitrary. In this study, four types of normalization methods for data preprocessing are presented, and these methods are applied in forecasting the displacement of bank landslides in the Three Gorges Reservoir area with various deformation mechanisms for comparison. The results demonstrate that (1) the selected normalization method substantially influences the forecast performance; (2) the normalization method is closely related to the selected forecasting model and is less dependent on the landslide deformation mechanism; and (3) the commonly used max-min normalization approach is not the optimal method, and the zero-mean normalization method is optimal for the particle swarm optimizer of support vector machine (PSO-SVM) method, while the logarithmic normalization method is optimal for the extreme learning machine method. The obtained results suggest that the data preprocessing methods must be carefully selected in landslide displacement forecasting.