TY  - JOUR
AU  - Zhao, Huanhuan
AU  - Zhao, Xiaole
AU  - Wen, Xueyi
AU  - Yan, Rongfang
AU  - Zhang, Jiandong
PY  - 2026
DA  - 2026/04/03
TI  - Slope Stability and Safety Assessment Based on Random Forest Enhanced under Multi-Strategy Pelican Optimization
JO  - ICCK Transactions on Systems Safety and Reliability
T2  - ICCK Transactions on Systems Safety and Reliability
JF  - ICCK Transactions on Systems Safety and Reliability
VL  - 2
IS  - 2
SP  - 82
EP  - 100
DO  - 10.62762/TSSR.2026.963232
UR  - https://www.icck.org/article/abs/TSSR.2026.963232
KW  - slope stability prediction
KW  - visualization analysis
KW  - RF
KW  - IPOA-RF
AB  - To improve the prediction accuracy of slope stability and prevent slope failure accidents, this study proposes a slope stability prediction model based on an improved pelican optimization algorithm optimized random forest (Improved Pelican Optimization Algorithm optimized Random Forest, IPOA-RF). First, according to 431 slope cases, the slope height, slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio were selected as the main predictive features. Second, due to the issue of excessive hyperparameters in the traditional random forest (RF) model, the IPOA algorithm was employed to optimize the RF parameters using an optimal-guidance strategy, mutation operator, and dynamically adjusted search mechanism. Finally, compared with five other optimization algorithms, the proposed IPOA algorithm exhibited superior parameter optimization ability and convergence performance in ten benchmark test functions. The designed IPOA-RF model achieved an average prediction accuracy of 85.1%, approximately 10.4% higher than that of the traditional RF model (74.7%). The results demonstrate that the IPOA-RF model can rapidly and accurately identify slope stability conditions, effectively overcoming the limitations of conventional methods. This model not only provides an innovative solution for slope stability assessment but also offers technical support for enhancing the safety and operational efficiency of practical slope engineering projects.
SN  - 3069-1087
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Zhao2026Slope,
  author = {Huanhuan Zhao and Xiaole Zhao and Xueyi Wen and Rongfang Yan and Jiandong Zhang},
  title = {Slope Stability and Safety Assessment Based on Random Forest Enhanced under Multi-Strategy Pelican Optimization},
  journal = {ICCK Transactions on Systems Safety and Reliability},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {82-100},
  doi = {10.62762/TSSR.2026.963232},
  url = {https://www.icck.org/article/abs/TSSR.2026.963232},
  abstract = {To improve the prediction accuracy of slope stability and prevent slope failure accidents, this study proposes a slope stability prediction model based on an improved pelican optimization algorithm optimized random forest (Improved Pelican Optimization Algorithm optimized Random Forest, IPOA-RF). First, according to 431 slope cases, the slope height, slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio were selected as the main predictive features. Second, due to the issue of excessive hyperparameters in the traditional random forest (RF) model, the IPOA algorithm was employed to optimize the RF parameters using an optimal-guidance strategy, mutation operator, and dynamically adjusted search mechanism. Finally, compared with five other optimization algorithms, the proposed IPOA algorithm exhibited superior parameter optimization ability and convergence performance in ten benchmark test functions. The designed IPOA-RF model achieved an average prediction accuracy of 85.1\%, approximately 10.4\% higher than that of the traditional RF model (74.7\%). The results demonstrate that the IPOA-RF model can rapidly and accurately identify slope stability conditions, effectively overcoming the limitations of conventional methods. This model not only provides an innovative solution for slope stability assessment but also offers technical support for enhancing the safety and operational efficiency of practical slope engineering projects.},
  keywords = {slope stability prediction, visualization analysis, RF, IPOA-RF},
  issn = {3069-1087},
  publisher = {Institute of Central Computation and Knowledge}
}