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Volume 17
Issue 19
10.3390/w17192882
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Short-Term Displacement Prediction of Rainfall-Induced Landslides Through the Integration of Static and Dynamic Factors: A Case Study of China

by
Chuyun Cheng
1,2,
Wenyi Zhao
3,4,5,
Lun Wu
1,2,
Xiaoyin Chang
1,2,
Bronte Scheuer
1,2,
Jianxue Zhang
1,2,
Ruhao Huang
1,2 and
Yuan Tian
1,2,*
1
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China
2
Beijing Key Laboratory of Spatial Information Integration and Its Applications, Beijing 100871, China
3
School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing 100083, China
4
China Institute of Geo-Environment Monitoring, Beijing 100081, China
5
Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(19), 2882; https://doi.org/10.3390/w17192882
Submission received: 12 September 2025 / Revised: 29 September 2025 / Accepted: 1 October 2025 / Published: 2 October 2025
(This article belongs to the Special Issue Water-Related Landslide Hazard Process and Its Triggering Events)
Versions Notes

Abstract

Rainfall-induced landslide deformation is governed by both intrinsic geological conditions and external dynamic triggers. However, many existing predictive models rely primarily on rainfall inputs, which limits their interpretability and robustness. To address these shortcomings, this study introduces a group-based data augmentation method informed by displacement curve morphology and proposes a multi-slope predictive framework that integrates static geological attributes with dynamic triggering factors. Using monitoring data from 274 sites across China, the framework was implemented with a Temporal Fusion Transformer (TFT) and benchmarked against baseline models, including SVR, XGBoost, and LSTM models. The results demonstrate that group-based augmentation enhances the stability and accuracy of predictions, while the integrated dynamic–static TFT framework delivers superior accuracy and improved interpretability. Statistical significance testing further confirms consistent performance improvements across all groups. Collectively, these findings highlight the framework’s effectiveness for short-term landslide forecasting and underscore its potential to advance early warning systems.
Keywords: landslide displacement; short-term prediction; static and dynamic factors; slope grouping; transfer learning; Temporal Fusion Transformer landslide displacement; short-term prediction; static and dynamic factors; slope grouping; transfer learning; Temporal Fusion Transformer

Share and Cite

MDPI and ACS Style

Cheng, C.; Zhao, W.; Wu, L.; Chang, X.; Scheuer, B.; Zhang, J.; Huang, R.; Tian, Y. Short-Term Displacement Prediction of Rainfall-Induced Landslides Through the Integration of Static and Dynamic Factors: A Case Study of China. Water 2025, 17, 2882. https://doi.org/10.3390/w17192882

AMA Style

Cheng C, Zhao W, Wu L, Chang X, Scheuer B, Zhang J, Huang R, Tian Y. Short-Term Displacement Prediction of Rainfall-Induced Landslides Through the Integration of Static and Dynamic Factors: A Case Study of China. Water. 2025; 17(19):2882. https://doi.org/10.3390/w17192882

Chicago/Turabian Style

Cheng, Chuyun, Wenyi Zhao, Lun Wu, Xiaoyin Chang, Bronte Scheuer, Jianxue Zhang, Ruhao Huang, and Yuan Tian. 2025. "Short-Term Displacement Prediction of Rainfall-Induced Landslides Through the Integration of Static and Dynamic Factors: A Case Study of China" Water 17, no. 19: 2882. https://doi.org/10.3390/w17192882

APA Style

Cheng, C., Zhao, W., Wu, L., Chang, X., Scheuer, B., Zhang, J., Huang, R., & Tian, Y. (2025). Short-Term Displacement Prediction of Rainfall-Induced Landslides Through the Integration of Static and Dynamic Factors: A Case Study of China. Water, 17(19), 2882. https://doi.org/10.3390/w17192882

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