收藏切换
Long Term Displacement Prediction of Landslides Based on GCformer
收藏切换
PDF
Wan-li YANG1, Yong-qiang HE2, Jian-liang ZHANG3, Hui-qin WANG3, *, Xiao-juan LI2
Science Technology and Engineering | 2025, 25(12) : 4913 - 4919
Less
收藏切换
Science Technology and Engineering | 2025, 25(12): 4913-4919
Papers·Astronomy and Geosciences
Long Term Displacement Prediction of Landslides Based on GCformer
Full
Wan-li YANG1, Yong-qiang HE2, Jian-liang ZHANG3, Hui-qin WANG3, *, Xiao-juan LI2
Affiliations
  • 1 Gansu Road and Bridge Highway Investment Co. , Ltd. , Lanzhou 730030, China
  • 2 School of Civil Engineering, Northwest Minzu University, Lanzhou 730030, China
  • 3 School of Computing and Communications, Lanzhou University of Technology, Lanzhou 730050, China
Published: 2025-04-28 doi: 10.12404/j.issn.1671-1815.2402357
Outline
收藏切换

To enhance the long-term displacement prediction accuracy of landslides, the GCformer model was applied to landslide displacement forecasting, and a novel landslide displacement prediction approach grounded in the GCformer model was proposed. This methodology leveraged rainfall and displacement as input variables, utilized the GConvmsk module to capture the global information of the sequence, and combined a linear scaling technique of sequence length to efficiently extract data features. Concurrently, the PatchTST model was employed to automatically extract short-term and long-term signals from the sequence data, in order to obtain more comprehensive historical information and bolster the model's robustness and modeling capability. Finally, the landslide displacement monitoring data from Jinliuping Village and Yuanshitan Village in Huichuan County, Dingxi City, Gansu Province, were utilized for case validation. The findings demonstrate that the proposed model exhibits superior prediction accuracy and reliability. In comparison to the Autoformer model and the FEDformer model, the GCformer model is found to achieve the lowest error in both total displacement and vertical displacement.

landslide displacement prediction  /  GCformer model  /  FEDformer model  /  Autoformer model
Wan-li YANG, Yong-qiang HE, Jian-liang ZHANG, Hui-qin WANG, Xiao-juan LI. Long Term Displacement Prediction of Landslides Based on GCformer[J]. Science Technology and Engineering, 2025 , 25 (12) : 4913 -4919 . DOI: 10.12404/j.issn.1671-1815.2402357
Year 2025 volume 25 Issue 12
PDF
312
134
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2402357
  • Receive Date:2024-04-02
  • Online Date:2025-07-09
  • Published:2025-04-28
Article Data
Affiliations
History
  • Received:2024-04-02
  • Revised:2025-01-22
Funding
Affiliations
    1 Gansu Road and Bridge Highway Investment Co. , Ltd. , Lanzhou 730030, China
    2 School of Civil Engineering, Northwest Minzu University, Lanzhou 730030, China
    3 School of Computing and Communications, Lanzhou University of Technology, Lanzhou 730050, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2402357
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT