## 1 Introduction  
Landslides are a type of geological disaster occurring on the crustal surface of the earth, and are regarded as serious hazards that are widely distributed throughout the globe [1]. They not only cause heavy casualties or property losses, but also serious social and environmental problems. Landslides result in significant harm to human beings mainly because it is difficult to accurately predict and forecast landslides in advance. In addition, because of financial constraints and other limitations, only an extremely small number of major landslides have been monitored, and control measures have not been taken for most landslides. However, in many cases, it is not easy to fully mine a large amount of useful information and accurately grasp the deformation and evolution of landslides, owing to a lack of effective data processing and prediction technology, even if the landslides are subjected to monitoring. Therefore, predicting the development process of large-scale landslides remains a contentious issue [1, 2, 3, 4, 5, 6]. Because of the complexity of a landslide body and the diversity and randomness of its influencing factors, an accurate description of its development law is complex. However, the series of landslide body displacements, which vary over time, can be used to describe the general laws of landslide development. Therefore, some researchers have proposed different methods for the prediction of landslide deformation and displacement [2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15]. The methods can be roughly classified into four categories--deterministic methods [2, 7, 8, 12], statistical methods [9, 11], numerical simulations [4, 10], and nonlinear methods [13, 14, 15]. Each method has its own features. The deterministic methods are based on creep theory and other physical mechanisms of rock and soil mass and can provide a clear physical concept; however, they have strict application conditions. The statistical methods are based on mathematical statistics and other prediction theories and can be used to obtain statistical laws from a known displacement to infer an unknown displacement, which are usable for landslides without clear physical mechanisms. Numerical simulation methods are based on simulation software or programs such as FLAC 3D to build a numerical-mechanical model of landslide deformation prediction, the parameters of which are not easy to determine. Nonlinear methods are presented by introducing some nonlinear theories (e.g., machine learning theory, synergetic theory, and chaotic dynamics theory), which have the potential for coping with difficult and complicated problems [5]. Moreover, these methods can also be divided into two categories--single- and multi-factor prediction methods. The former only considers previous displacement or deformation, whereas the latter considers inducing factors in addition to landslide displacement or deformation. Thus far, most of the methods mentioned above have applied single-factor prediction models and can only be established through a single factor, such as landslide displacement or deformation. As is well known, the evolution of landslides can be influenced by a variety of factors such as rainfall and earthquakes. Multi-factor prediction models will be a good choice for landslide displacement prediction in the future. In recent years, machine learning methods such as an artificial neural network (ANN) and support vector machine (SVM) are being widely applied in landslide research because they can consider the influences of multiple complex factors on landslides and achieve good performance with regard to providing predictions, extracting associated features, and making decisions from the given information [13; 14; 15; 16; 17; 18; 19; 20]. Among them, an SVM, a novel machine-learning algorithm presented by [PERSON] (1995), is capable of preferentially resolving small-sample, nonlinear, and high-dimensional problems, while considering the influence of multiple factors.