**5. Conclusions**

This research develops a new deep-learning approach for landslide displacement forecasting called GC-GCN-N, which combines the GCN and the GRU. The architecture inherits the merits from both GCN in extracting spatial dependencies and GRU in capturing temporal correlation features to tackle the spatiotemporal landslide displacement forecast. In the proposed model, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all monitoring stations, (2) a feature matrix is assembled to handle the time-series measurements of all monitoring stations, (3) an attribute-augmented unit is designed to represent the effects of the triggering factors and integrate the matrix mentioned above into a single graph convolutional network, and (4) a novel neural network-based approach is developed to enable to process the above graph-structured data. Experiments have been carried out on two landslides in Three George Reservoir, China. Compared with the MLR model, the ARIMA model, the SVR model, the LSTM model, and the T-GCN model, the GC-GCN-N model outperforms other forecasting models at both landslide sites. In summary, the GC-GCN-N model successfully captures the spatial and temporal features from the landslide monitoring dataset, showing great potential for other spatiotemporal forecast tasks.

**Author Contributions:** Conceptualization, Q.X. and Y.J.; methodology and validation, Y.J. and H.L. (Huiyuan Luo); formal analysis and investigation, L.L. resources, Q.X. and H.L.(Huajin Li); writing—original draft preparation, Y.J.; writing—review and editing, Y.J. and Z.L.; visualization, H.L. (Huiyuan Luo); funding acquisition, Q.X., L.H. and Y.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is financially supported by the National Key Research and Development Program of China (2018YFC1505101, 2021YFC3000401). The Key Research and Development Program of Sichuan Province (2020YFS0353) and the Open Fund of State Key Laboratory of Geohazard Prevention and Geoenviroment Protection (SKLGP2017K016).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The raw data used in this study can be downloaded for scientific research after approval at http://www.crensed.ac.cn/, accessed on 14 February 2022.

**Acknowledgments:** The authors would like to thank the National Service Center for Specialty Environmental Observation Stations for providing the in situ monitoring data set.

**Conflicts of Interest:** The authors declare no conflict of interest.
