## 1 Introduction
The common deep learning approaches for time-series forecasting mainly include deep neural networks (DNNs) [21; 22], convolutional neural networks (CNNs) [23], recurrent neural networks (RNNs) [24] and their variant models, as well as the transformer. Among them, RNNs have a strong model fitting ability for serialized data; and the most widely used RNN variants are the long-short-term memory (LSTM) [25] and gate recurrent unit (GRU) [26], which improve the shortcoming of the gradient explosion problem in conventional RNNs. The LSTM and GRU are more effective on long sequence data than conventional RNNs and have achieved satisfactory prediction results in many engineering applications [27; 28]. For example, [PERSON] et al. [25] proposed an LSTM-based dynamic prediction model for landslide displacement and verified the performance of the model with observed data from two stepped landslides. [PERSON] et al. [29] presented a novel deep learning method, T-GCN, which combined a GCN and a GRU to predict the slope deformation. However, when the input sequence is long, the performance of the LSTM model is still poor. LSTM models that include an attention mechanism are effective to improve the performance of the LSTM and have been applied to slope deformation in recent years [24; 30; 31]. The transformer is a new deep learning model proposed in 2017. The transformer model replaces the traditional sequential structure of RNNs with a self-attention mechanism, which allows the model to be trained in parallel and have access to global information. Currently, transformer models have been successfully applied to time-series forecasting problems [32; 33]. However, few studies have applied the transformer to slope deformation prediction. Currently, most studies for slope deformation focus on individual machine learning approaches compared to the ARIMA to show the nonlinear prediction performance of machine learning approaches. However, there are significant performance differences among different machine learning approaches, and few studies have conducted a comprehensive comparative analysis of the common machine learning models. In addition, some of the latest deep learning methods, such as the transformer, although outstanding in time series prediction problems in other fields, have hardly been studied for and applied to slope deformation prediction. Each model has different characteristics and application scenarios; thus, choosing an accurate model for slope deformation prediction is critical. To address the above problem, in this paper, we conducted a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. The contributions in this paper can be summarized as follows:  
1. We established two traditional machine learning models of the SVR and XGBoost and eight deep learning models, including the conventional DNN, CNN, RNN, LSTM, BiLSTM, Attention-LSTM, GRU, and transformer, while employing the linear ARIMA model as a baseline for comparison. The displacement data of seven in situ monitoring points of the Huanglianshu landslide located in eastern Chongqing were used as an example for verification. 2. We comparatively analyzed the prediction performance of the employed models based on four error metrics and provided a suggestion for selecting an appropriate prediction model for slope deformation. The rest of this paper is organized as follows. Section 2 introduces the overview of the study area. Section 3 describes the applied prediction models in detail. Section 4 describes the predicted results of the models. Section 5 comparatively analyses the results. Section 6 concludes the paper.