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Landslide Displacement Prediction Model Based on Optimized Time Series Decomposition and Feature Selection
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Dongmin WANG1, Lihua ZHAO1, *, Wei QU1, Zimu HANG1, Li WANG1
Journal of Geodesy and Geodynamics | 2026, 46(6) : 748 - 757
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Journal of Geodesy and Geodynamics | 2026, 46(6): 748-757
Landslide Displacement Prediction Model Based on Optimized Time Series Decomposition and Feature Selection
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Dongmin WANG1, Lihua ZHAO1, *, Wei QU1, Zimu HANG1, Li WANG1
Affiliations
  • 1 School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
Published: 2026-06-15 doi: 10.14075/j.jgg.2025.08.293
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Aiming at the problem that it is difficult for the temporal decomposition model to accurately distinguish the effects of induced factors on different displacement components, and the prediction accuracy is insufficient under the uncertainty of meteorological data, a landslide displacement prediction network model based on optimized time series decomposition and feature selection is proposed. Firstly, the variational modal decomposition (GA-VMD) method optimized by singular spectral analysis (SSA) and genetic algorithm is combined with induced factors to decompose the landslide displacement. Subsequently, an improved Nishihara model with fusion inducible factors is constructed to predict the trend term displacement, and the combined network of convolutional neural network and gated recurrent unit (CNN-SE-GRU) combined with compression and excitation network was used to model the period term displacement, and the random term displacement is reconstructed through frequency domain analysis. Finally, the probability interval of the displacement prediction results is constructed by combining kernel density estimation (KDE) and Monte Carlo simulation. Taking the Heifangtai landslide in Gansu province as an example, the RMSE and MAPE of the prediction model are 1.52 mm and 0.38%, respectively, and the prediction accuracy of the model is significantly improved compared with the traditional prediction model, providing more reliable technical support for landslide early warning.

landslide displacement prediction  /  time series decomposition  /  feature selection  /  neural network  /  error propagation modeling
Dongmin WANG, Lihua ZHAO, Wei QU, Zimu HANG, Li WANG. Landslide Displacement Prediction Model Based on Optimized Time Series Decomposition and Feature Selection[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (6) : 748 -757 . DOI: 10.14075/j.jgg.2025.08.293
Year 2026 volume 46 Issue 6
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doi: 10.14075/j.jgg.2025.08.293
  • Receive Date:2025-08-24
  • Online Date:2026-07-09
  • Published:2026-06-15
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  • Received:2025-08-24
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    1 School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
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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
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