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.

数据预处理是滑坡位移预测中必不可少的步骤，是预测滑坡变形和破坏行为的有效方法。但是，大多数研究集中在位移预测模型的构建上，而忽略了数据预处理对预测结果的影响。数据规范化是数据预处理的重要部分。但是，数据标准化方法的选择是主观的和任意的。在这项研究中，提出了四种类型的数据预处理归一化方法，并将这些方法用于预测三峡水库库岸滑坡的位移，并通过各种变形机制进行比较。结果表明：（1）选择的归一化方法对预测性能有较大影响；（2）归一化方法与所选的预测模型密切相关，对滑坡变形机制的依赖性较小；（3）常用的max-min归一化方法不是最优方法，对于支持向量机（PSO-SVM）方法的粒子群优化器，零均值归一化方法是最优的，而对数归一化方法是最优的用于极限学习机的方法。获得的结果表明，在滑坡位移预测中必须谨慎选择数据预处理方法。支持向量机（PSO-SVM）方法的粒子群优化器为零均值归一化方法，极限学习机方法为对数归一化方法。获得的结果表明，在滑坡位移预测中必须谨慎选择数据预处理方法。支持向量机（PSO-SVM）方法的粒子群优化器为零均值归一化方法，极限学习机方法为对数归一化方法。获得的结果表明，在滑坡位移预测中必须谨慎选择数据预处理方法。