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Slope stability prediction model based on multi-strategy improved SSA for optimizing KELM
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Yun QI1, 2, 3, Kailong XUE4, 5, **, Xuping LI1, 2, 4, Wei WANG1, 2, 3, Chenhao BAI5, Zhunze JI6
China Safety Science Journal | 2025, 35(3) : 92 - 98
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China Safety Science Journal | 2025, 35(3): 92-98
• Safety engineering technology •
Slope stability prediction model based on multi-strategy improved SSA for optimizing KELM
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Yun QI1, 2, 3, Kailong XUE4, 5, **, Xuping LI1, 2, 4, Wei WANG1, 2, 3, Chenhao BAI5, Zhunze JI6
Affiliations
  • 1 School of Mining and Coal，Inner Mongolia University of Science and Technology，Baotou Inner Mongolia 014010，China
  • 2 Inner Mongolia Key Laboratory of Mining Engineering，Baotou Inner Mongolia 014010，China
  • 3 Inner Mongolia Research Center for Coal Safety Mining and Utilization Engineering and Technology，Baotou Inner Mongolia 014010，China
  • 4 Inner Mongolia Cooperative Innovation Center for Coal Green Mining and Green Utilization，Baotou Inner Mongolia 014010，China
  • 5 School of Coal Engineering，Shanxi Datong University，Datong Shanxi 037000，China
  • 6 School of Materials and Metallurgy，Inner Mongolia University of Science and Technology，Baotou Inner Mongolia 014010，China
Published: 2025-03-28 doi: 10.16265/j.cnki.issn1003-3033.2025.03.0134
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In order to predict the slope state more accurately and effectively prevent the slope instability accident，an improved ISSA-KELM slope stability prediction model was proposed. Firstly，six main factors such as bulk density and cohesion in slope instability characteristics were used as prediction indexes to establish a data set for slope stability evaluation. Secondly，SSA was enhanced by incorporating Sine chaotic mapping，Levy flight strategy，dynamic adaptive weights，and fusion of optimal explosion strategy and reverse learning. These improvements aimed at enhancing the global search capability and stability of SSA. Subsequently，ISSA was employed to optimize the kernel parameter ψ and regularization coefficient C in KELM for improved prediction accuracy while avoiding overfitting issues associated with KELM. The results show that the accuracy rate，precision，recall rate and F1 score of ISSA-KELM model reached 0.945 9，1，0.866 7 and 0.929，respectively，which are superior to SSA-KELM，PSO-KELM and PSO-SVM models，and the predicted results of the model are the closest to the actual values. It shows that the established ISSA-KELM model has strong generalization ability.

slope stability  /  prediction model  /  improved sparrow search algorithm (ISSA)  /  kernel extreme learning machine (KELM)  /  prediction index  /  confusion matrix
Yun QI, Kailong XUE, Xuping LI, Wei WANG, Chenhao BAI, Zhunze JI. Slope stability prediction model based on multi-strategy improved SSA for optimizing KELM[J]. China Safety Science Journal, 2025 , 35 (3) : 92 -98 . DOI: 10.16265/j.cnki.issn1003-3033.2025.03.0134
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Year 2025 volume 35 Issue 3
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.03.0134
  • Receive Date：2024-10-20
  • Online Date：2025-07-05
  • Published：2025-03-28
Article Data
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History
  • Received：2024-10-20
  • Revised：2024-12-28
Funding
Affiliations
    1 School of Mining and Coal，Inner Mongolia University of Science and Technology，Baotou Inner Mongolia 014010，China
    2 Inner Mongolia Key Laboratory of Mining Engineering，Baotou Inner Mongolia 014010，China
    3 Inner Mongolia Research Center for Coal Safety Mining and Utilization Engineering and Technology，Baotou Inner Mongolia 014010，China
    4 Inner Mongolia Cooperative Innovation Center for Coal Green Mining and Green Utilization，Baotou Inner Mongolia 014010，China
    5 School of Coal Engineering，Shanxi Datong University，Datong Shanxi 037000，China
    6 School of Materials and Metallurgy，Inner Mongolia University of Science and Technology，Baotou Inner Mongolia 014010，China
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红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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