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Real-time prediction model for landslide displacement based on PSO-GRU-OL-MRT
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Yufeng TANG1, 2, Liqiu HE1, Qingyuan XIONG2, Yaling XIONG2, Yanqiu SHI2, Guangzhong HU**, 1
China Safety Science Journal | 2025, 35(12) : 70 - 77
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China Safety Science Journal | 2025, 35(12): 70-77
Safety engineering technology
Real-time prediction model for landslide displacement based on PSO-GRU-OL-MRT
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Yufeng TANG1, 2, Liqiu HE1, Qingyuan XIONG2, Yaling XIONG2, Yanqiu SHI2, Guangzhong HU**, 1
Affiliations
  • 1School of Mechanical Engineering, Sichuan University of Science & Engineering, Yibin Sichuan 644005, China
  • 2Sichuan Yuande Safety & Security Detection Equipment Co., Ltd., Zigong Sichuan 643000, China
Published: 2025-12-28 doi: 10.16265/j.cnki.issn1003-3033.2025.12.0023
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To address the challenges that static prediction models face in accurately forecasting dynamic landslide trends and the high computational costs associated with dynamic models—which hinder real-time prediction—this study proposes a novel real-time model for landslide displacement prediction. The model integrated Particle Swarm Optimization (PSO), a Gated Recurrent Unit (GRU) network, Online Learning (OL), and dynamic Model Retraining (MRT). First, a static landslide prediction model was established by integrating PSO and GRU. Second, an OL strategy was incorporated into the static model, enabling dynamic updates and real-time predictions as new monitoring data were acquired. Then, small-batch MRT was performed based on prediction accuracy evaluation to predict landslide trends dynamically and in real time. Finally, a comparative analysis of several related models was conducted using the Wangeryan landslide in Sichuan Province as a case study. The results indicate that the OL and MRT methods significantly improve prediction accuracy. Specifically, the PSO-GRU-OL-MRT model achieved Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R2 values of 0.795, 3.53, 1.40, and 0.954, respectively, with an average prediction time of 25.0 seconds per instance, demonstrating the highest prediction accuracy. In comparison, the GRU-OL-MRT model yielded values of 1.73, 7.82, 2.54, and 0.917 for the same four metrics, with an average prediction time of 0.496 seconds per instance, significantly reducing computational costs while maintaining relatively high prediction accuracy.

particle swarm optimization (PSO)  /  gated recurrent unit (GRU)  /  online learning (OL)  /  dynamic model re-train (MRT)  /  landslide displacement  /  real-time prediction
Yufeng TANG, Liqiu HE, Qingyuan XIONG, Yaling XIONG, Yanqiu SHI, Guangzhong HU. Real-time prediction model for landslide displacement based on PSO-GRU-OL-MRT[J]. China Safety Science Journal, 2025 , 35 (12) : 70 -77 . DOI: 10.16265/j.cnki.issn1003-3033.2025.12.0023
Year 2025 volume 35 Issue 12
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.12.0023
  • Receive Date:2025-08-06
  • Online Date:2026-07-09
  • Published:2025-12-28
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  • Received:2025-08-06
  • Revised:2025-10-10
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    1School of Mechanical Engineering, Sichuan University of Science & Engineering, Yibin Sichuan 644005, China
    2Sichuan Yuande Safety & Security Detection Equipment Co., Ltd., Zigong Sichuan 643000, 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
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种数
Number of
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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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