*4.1. Advantage of the Proposed Method*

Unlike the time-series forecast models that only explore temporal features and focus on a single point, this paper presents a new deep learning architecture that considers the spatial and temporal correlation for landslide displacement prediction. More specifically, the spatial correlation of the entire monitoring system and the temporal dependency of the monitoring time series are explored to establish the forecast model predicting the displacement of the monitoring network instead of a specific station. Considering the displacement prediction of a landslide relies not only on historical GNSS measurements and the spatial correlations of the monitoring network but also on various external incentive factors. An attribute-augmented unit is designed to integrate weighted adjacency matrix, displacements, and triggering factors to enhance the capture of spatial–temporal dependency serving as the model inputs.

To the best of the authors' knowledge, there is currently no related work focusing on addressing the prediction of rainfall reservoir-induced landslide displacement from a holistic perspective combining the external incentive factors. This paper presents a new deep learning GC-GCN-N model based on the GCN and GRU models, which effectively utilizes the spatial and temporal features contained in the model input data. The results show that the proposed model outperforms comparative models in both landslides over our study site in China's Three Gorge Reservoir (TGR).

#### *4.2. Shortcoming and Outlook of the Proposed Method*

As shown in Figures 6 and 7, several GNSS-monitored displacements show mutational transitions in September. Accordingly, significant prediction error often appears at this abrupt state (Figures 12–14), which is true to other forecast models. Considering the monthly data-acquisition limitation, this could be due to fewer samples available for the mutation state than for the other states. Thus, the model's errors probably gradually decrease as the number of samples for the mutation state increases. In addition, the GCN captures spatial features by constantly moving a smooth filter in the Fourier domain, which might also lead to the peaks being smoother.

Limited datasets in geohazard domains might be a prevalent phenomenon. Results of the Shuping landslide and the Baishuihe landslide also show that the number of motoring stations in a GNSS network also affects the prediction result. As monitoring equipment and data transmission technology advance, daily, hourly, and even minute-scale displacements could be collected and predicted in real time. Additionally, several other solutions have emerged in different domains for handling dataset limitations, including data augmentation [34], synthetic data [35], and transfer learning [36].

Data augmentation refers to increasing the number of data points without changing the label. For example, variable factors include random noise, and adequate time characteristics can be employed to enlarge the time-series data [34]. Although not real data, synthetic data contain the same patterns and statistical properties as actual data, generated by a deep-learning model called generative adversarial networks (GAN) [35]. Transfer learning uses knowledge from other relevant datasets or an existing model to construct new models that lack enough training data to provide an alternative solution [23,36].

In this study, periodic rainfall and reservoir water level fluctuations are the main factors triggering landslide kinematic evolution in the TGR area. Therefore, we consider only these two trigging factors. Subsequent studies might include more complicated datasets to establish a more comprehensive model. For example, factors affecting landslide motion can consist of other essential characteristics of landslides, such as strata lithology, slope aspect and angle, etc.
