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Earthquake Emergency Eupplies Demand Forecasting Based on PSO-SVM-SST Model
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Yandong TANG1, 2, Mei CHENG1, *, Jun LIU3, Xi YU1, 2, Hao LIN1
Journal of Geodesy and Geodynamics | 2026, 46(1) : 86 - 93
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Journal of Geodesy and Geodynamics | 2026, 46(1): 86-93
Earthquake Emergency Eupplies Demand Forecasting Based on PSO-SVM-SST Model
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Yandong TANG1, 2, Mei CHENG1, *, Jun LIU3, Xi YU1, 2, Hao LIN1
Affiliations
  • 1 Institute of Disaster Prevention, Academy of Emergency Management, Sanhe 065000, China
  • 2 Hebei Key Laboratory of Resource and Environmental Disaster Mechanism and Risk Monitoring, Sanhe 065000, China
  • 3 National Earthquake Response Support Service, Beijing 100049, China
Published: 2026-01-15 doi: 10.14075/j.jgg.2025.01.011
Outline
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A post-earthquake affected population prediction model based on support vector machines(SVM) optimized by particle swarm optimization(PSO) is established, and the SST(safety stock theory) earthquake emergency supply demand prediction model is constructed. Nine indicator parameters, including seismic hazard and damage severity, are selected and processed through dimensionality reduction and redundancy removal as input variables for the PSO-optimized SVM model to predict the affected population. Based on the relationship between the affected population and emergency supplies in disaster areas, the SST model is applied to indirectly estimate the quantities of typical supplies required in the immediate aftermath of the Jiuzhaigou earthquake. The experimental results are as follows: By employing an error comparison analysis method to validate the model's effectiveness, the PSO-SVM model demonstrates a 14.27% reduction in prediction error compared to the SVM model, with a significant improvement in prediction accuracy. The estimated demand for typical supplies in the aftermath of the Jiuzhaigou earthquake provides a certain degree of reference, indicating that the PSO-SVM-SST prediction model possesses both theoretical and practical rationality and utility.

earthquake emergency supplies  /  demand forecasting  /  support vector machine(SVM)  /  safety stock theory
唐彦东, 程梅, 刘军, 于汐, 林浩. 基于PSO-SVM-SST模型的地震应急物资需求预测研究. 大地测量与地球动力学, 2026 , 46 (1) : 86 -93 . DOI: 10.14075/j.jgg.2025.01.011
Yandong TANG, Mei CHENG, Jun LIU, Xi YU, Hao LIN. Earthquake Emergency Eupplies Demand Forecasting Based on PSO-SVM-SST Model[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (1) : 86 -93 . DOI: 10.14075/j.jgg.2025.01.011
Year 2026 volume 46 Issue 1
30
11
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doi: 10.14075/j.jgg.2025.01.011
  • Receive Date:2025-01-14
  • Online Date:2026-10-29
  • Published:2026-01-15
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  • Received:2025-01-14
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Affiliations
    1 Institute of Disaster Prevention, Academy of Emergency Management, Sanhe 065000, China
    2 Hebei Key Laboratory of Resource and Environmental Disaster Mechanism and Risk Monitoring, Sanhe 065000, China
    3 National Earthquake Response Support Service, Beijing 100049, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
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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