Interval Prediction of Landslide Displacement Using a Pretrained Large Time Series Model Based on Large-scale Cross-domain Data

Xuhuang  Du; Cheng  Lian; Bo  Xu; Zhiyong  Qi; Jin  Yuan; You  Mou; Bing  Li

doi:10.6180/jase.202512_28(12).0007

Interval Prediction of Landslide Displacement Using a Pretrained Large Time Series Model Based on Large-scale Cross-domain Data

Computer Science and Information Engineering

Xuhuang Du^1,2, Cheng Lian³This email address is being protected from spambots. You need JavaScript enabled to view it., Bo Xu^1,2, Zhiyong Qi^1,2, Jin Yuan^1,2, You Mou^1,2, and Bing Li²

¹Hubei Technology Innovation Center for Smart Hydropower

²China Yangtze Power Co., Ltd. (CYPC)

³School of Automation, Wuhan University of Technology

Received: October 9, 2024
Accepted: March 6, 2025
Publication Date: April 4, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202512_28(12).0007

Assessing the uncertainty in landslide displacement prediction is essential for improving the dependability of landslide early warning systems. In this study, we employ advanced Transformer-based models to develop robust probabilistic forecasting methods, which are used to construct reliable prediction intervals for landslide displacement. Furthermore, to overcome the challenge of limited data availability, a common issue due to the high costs associated with monitoring landslide displacement, we leverage Lag-LLama, a large pretrained model initially trained on extensive cross-domain time series data, to enhance the probabilistic prediction of landslide displacement. Our experiments, conducted on six real-world landslide displacement datasets from China, demonstrate that the Lag-LLama-based approach significantly outperforms state-of-the-art time series forecasting models in the context of landslide displacement interval prediction. These results highlight the potential of large pre-trained models in addressing data scarcity and improving predictive accuracy in geohazard monitoring applications.

Keywords: Landslide displacement prediction; Intervals prediction; Large pre-trained model; Transfer learning; Transformer.

[1] E. Miccadei, C. Carabella, and G. Paglia. Landslide Hazard and Environment Risk Assessment. 2022.
[2] M.Krkaˇ c, D. Špoljari´ c, S. Bernat, and S. M. Arbanas, (2017) “Method for prediction of landslide movements based on random forests" Landslides 14: 947–960.
[3] W. Chen, H. Shahabi, S. Zhang, K. Khosravi, A. Shirzadi, K. Chapi, B. T. Pham, T. Zhang, L. Zhang, H. Chai, et al., (2018) “Landslide susceptibility modeling based on gis and novel bagging-based kernel logistic regression" Applied Sciences 8(12): 2540.
[4] X. Zhu, Q. Xu, M.Tang, H. Li, and F. Liu, (2018) “A hybrid machine learning and computing model for fore casting displacement of multifactor-induced landslides" Neural Computing and Applications 30: 3825–3835.
[5] P. Zuan and Y. Huang, (2018) “Prediction of sliding slope displacement based on intelligent algorithm" Wireless Personal Communications 102: 3141–3157.
[6] J. Xu, Y. Jiang, and C. Yang, (2022) “Landslide displace ment prediction during the sliding process using XGBoost, SVRand RNNs"Applied Sciences 12(12): 6056.
[7] H.Wang, G.Long, P. Shao, Y. Lv, F. Gan, and J. Liao, (2023) “A DES-BDNN based probabilistic forecasting approach for step-like landslide displacement" Journal of Cleaner Production 394: 136281.
[8] W.Gong, S. Tian, L. Wang, Z. Li, H. Tang, T. Li, and L. Zhang, (2022) “Interval prediction of landslide dis placement with dual-output least squares support vector machine and particle swarm optimization algorithms" Acta Geotechnica 17(9): 4013–4031.
[9] Y. Xing, J. Yue, C. Chen, D. Cai, J. Hu, and Y. Xi ang, (2021) “Prediction interval estimation of landslide displacement using adaptive chicken swarm optimization tuned support vector machines" Applied intelligence: 1–18.
[10] Q. Ge, H. Sun, Z. Liu, B. Yang, S. Lacasse, and F. Nadim, (2022) “A novel approach for displacement inter val forecasting of landslides with step-like displacement pattern" Georisk: assessment and management of risk for engineered systems and geohazards 16(3): 489–503.
[11] J. Ma, H. Tang, X. Liu, T. Wen, J. Zhang, Q. Tan, and Z. Fan, (2018) “Probabilistic forecasting of landslide dis placement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area, China" Land slides 15: 1145–1153.
[12] Y. Liu, C. Li, J. Wang, and M. Long, (2024) “Koopa: Learning non-stationary time series dynamics with koop manpredictors" Advances in Neural Information Processing Systems 36:
[13] A. Das, W. Kong, A. Leach, S. Mathur, R. Sen, and R. Yu, (2023) “Long-term forecasting with tide: Time-series dense encoder" arXiv preprint arXiv:2304.08424:
[14] H. Wu, T. Hu, Y. Liu, H. Zhou, J. Wang, and M. Long, (2022) “Timesnet: Temporal 2d-variation mod eling for general time series analysis" arXiv preprint arXiv:2210.02186:
[15] M.Liu,A.Zeng,M.Chen,Z.Xu,Q.Lai,L.Ma,andQ. Xu, (2022) “Scinet: Time series modeling and forecasting with sample convolution and interaction" Advances in Neural Information Processing Systems 35: 5816–5828.
[16] H.Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang. “Informer: Beyond efficient trans former for long sequence time-series forecasting”. In: Proceedings of the AAAI conference on artificial intelligence. 35. 12. 2021, 11106–11115.
[17] H. Wu, J. Xu, J. Wang, and M. Long, (2021) “Auto former: Decomposition transformers with auto-correlation for long-term series forecasting" Advances in neural information processing systems 34: 22419–22430.
[18] T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin. “Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting”. In: In ternational conference on machine learning. PMLR. 2022, 27268–27286.
[19] Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, (2022) “A time series is worth 64 words: Long-term forecasting with transformers" arXiv preprint arXiv:2211.14730:
[20] Y. Liu, T. Hu, H. Zhang, H. Wu, S. Wang, L. Ma, and M. Long, (2023) “itransformer: Inverted transformers are effective for time series forecasting" arXiv preprint arXiv:2310.06625:
[21] A. Vaswani, (2017) “Attention is all you need" arXiv preprint arXiv:1706.03762:
[22] K. Rasul, A. Ashok, A. R. Williams, H. Ghonia, R. Bhagwatkar, A. Khorasani, M. J. D. Bayazi, G. Adamopoulos, R. Riachi, N. Hassen, M. Biloš, S. Garg, A. Schneider, N. Chapados, A. Drouin, V. Zant edeschi, Y. Nevmyvaka, and I. Rish. Lag-Llama: To wards Foundation Models for Probabilistic Time Series Forecasting. 2024. arXiv: 2310.08278 [cs.LG].
[23] H.Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample. LLaMA: Open and Efficient Foundation Language Models. 2023. arXiv: 2302.13971 [cs.CL].
[24] B. Zhang and R. Sennrich. Root Mean Square Layer Normalization. 2019. arXiv: 1910.07467 [cs.LG].
[25] J. Su, Y. Lu, S. Pan, A. Murtadha, B. Wen, and Y. Liu. RoFormer: Enhanced Transformer with Rotary Position Embedding. 2023. arXiv: 2104.09864 [cs.CL].
[26] M.Xia, G. M. Ren, and X. L. Ma, (2013) “Deformation and mechanism of landslide influenced by the effects of reservoir water and rainfall, Three Gorges, China" Natu ral Hazards 68: 467–482.
[27] C.Lian, Z. Zeng, W. Yao, and H. Tang, (2014) “Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis" Neural Com puting and Applications 24: 99–107.
[28] F. Ren, X. Wu, K. Zhang, and R. Niu, (2015) “Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China" Environ mental Earth Sciences 73: 4791–4804.
[29] C. Lian, Z. Zeng, W. Yao, and H. Tang, (2015) “Mul tiple neural networks switched prediction for landslide displacement" Engineering geology 186: 91–99.
[30] S. Xu and R. Niu, (2018) “Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China" Computers & Geosciences 111: 87–96.
[31] C. Lian, C. P. Chen, Z. Zeng, W. Yao, and H. Tang, (2016) “Prediction intervals for landslide displacement based on switched neural networks" IEEE Transactions on Reliability 65(3): 1483–1495.