An Intelligent Hybrid System for Slope Stability Prediction: Integration of Random Forest with WOA, ACO and GWO Algorithms
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Abstract
This study aims to develop and evaluate hybridised models combining Random Forest (RF) with metaheuristic algorithms, Whale Optimisation Algorithm (WOA), Ant Colony Optimisation (ACO), and Grey Wolf Optimisation (GWO), to predict the Factor of Safety (FS) in slope stability analysis. The study used a dataset of 281 data points from the AngloGold Ashanti Iduapriem Mine, comprising seven input parameters and FS as the output. Three hybrid models (RF-WOA, RF-ACO and RF-GWO) were developed and compared using performance indicators including mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). A sensitivity analysis was conducted to determine the most influential parameters affecting the prediction of FS. The RF-WOA model outperformed the other hybrid models, achieving the lowest MAE (0.0416), MSE (0.0086) and highest R² (0.9656). Sensitivity analysis revealed that cohesion is the most critical factor influencing slope stability, followed by the slope height and pore water pressure. The rock mass rating had minimal impact on the prediction of the FS. This study presents a novel approach by integrating metaheuristic algorithms with Random Forest for slope stability analysis, offering improved predictive performance compared to traditional methods. The developed RF-WOA model provides mining engineers with a reliable tool for predicting slope stability, enabling more effective risk management and operational planning in open-pit mines. The identified key influencing factors can guide prioritisation in geotechnical assessments and slope design considerations.
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Abbas, J. (2014). Slope Stability Analysis using Numerical Method. 14(9), 846–859. https://doi.org/10.3923/jas.2014.846.859.
Acheampong, E. J. (2009). The Effects of Rainfall on Production at AngloGold Ashanti Iduapriem Mine. Unpublished BSc Project Report, University of Mines and Technology (UMaT), Tarkwa, Ghana, 34.
Alzakkar, A., Mestnikov, N. P., Maksimov, V. V., & Valeev, I. M. (2022). Assessment of the voltage stability index of electric networks supplying charging stations using a multilayer perceptron. Proceedings of the Higher Educational Institutions Energy Sector Problems, 24(2), 35–48. https://doi.org/10.30724/1998-9903-2022-24-2-36-49
Boateng, F. O. (2009). Assessing the Groundwater Quality of an In-Pit Tailings Impoundment of Block 1 and 2 at AngloGold Ashanti (AGA) Iduapriem Mine. Unpublished BSc Project Report University of Mines and Technology (UMaT), Tarkwa, Ghana, 35.
Bobet, A. (2010). Numerical Methods in Geomechanics. 35(1), 27. https://www.semanticscholar.org/paper/Numerical-Methods-in-Geomechanics-Bobet/2001219662ddc2c1ee3606a758359cc9406757fa
Bolton, H., Heymann, G., & Groenwold, A. (2003). Global search for critical failure surface in slope stability analysis. Engineering Optimization, 35(1), 51–65. https://doi.org/10.1080/0305215031000064749
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
Chen, W., & Yang, Z. (2023). Landslide susceptibility modeling using bivariate statistical-based logistic regression, naïve Bayes, and alternating decision tree models. Bulletin of Engineering Geology and the Environment, 82(5). https://doi.org/10.1007/s10064-023-03216-1
Chen, W.-F. (2013). Limit analysis and Soil Plasticity. Elsevier. https://books.google.com/books?hl=en&lr=&id=FpkQfO4kzB8C&oi=fnd&pg=PP1&dq=Limit+Analysis+and+Soil+Plasticity:+Developments+in+Geotechnical+Engineering&ots=dYZ3GlAsrN&sig=AG9c7_wDxzgGNv_A6KZO4bLmvBc
Digvijay, S., Guruprasd, C., Rupa, N. B., & Pooja, R. K. (2017). An overview on methods for slope stability analysis. International Journal of Engineering Research & Technology (Ahmedabad), 6(3). https://doi.org/10.17577/IJERTV6IS030496
Dorigo, M., & Gambardella, L. M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation : A Publication of the IEEE Neural Networks Council, 1(1), 53–66. https://doi.org/10.1109/4235.585892
Dorigo, M., & Stützle, T. (2019). Ant Colony Optimization: Overview and Recent Advances. In Handbook of Metaheuristics (pp. 311–351). Springer International Publishing. https://doi.org/10.1007/978-3-319-91086-4_10
Feng, X., Li, S., Yuan, C., Zeng, P., & Sun, Y. (2018). Prediction of Slope Stability using Naive Bayes Classifier. KSCE Journal of Civil Engineering, 22(3), 941–950. https://doi.org/10.1007/s12205-018-1337-3
Girard, J. M., & Mc Hugh, E. (2000). Detecting problems with mine slope stability. In Bockosh GR, Karmis M, Langton J, McCarter MK, Rowe B (Ed.), Thirty-first Annual Institute on Mining Health, Safety and Research (pp. 147–155). Virginia Polytechnic Institute and State University.
Griffiths, D. V., & Lane, P. A. (2000). Assessment of stability of slopes under drawdown conditions. Journal of Geotechnical and Geoenvironmental Engineering, 126(5), 443–450. https://doi.org/10.1061/(ASCE)1090-0241(2000)126:5(443)
Hou, W., Xiong, Z., Wang, C., & Chen, H. (2022). Enhanced Ant Colony Algorithm with Communication Mechanism for Mobile Robot Path Planning. Robotics and Autonomous Systems, 148(103949), 103949. https://doi.org/10.1016/j.robot.2021.103949
Kahatadeniya, K. S., Nanakorn, P., & Neaupane, K. M. (2009). Determination of the critical failure surface for slope stability analysis using ant colony optimization. Engineering Geology, 108(1–2), 133–141. https://doi.org/10.1016/j.enggeo.2009.06.010
Kesse, G. O. (1985). The Mineral and Rock Resources of Ghana. 610. https://www.osti.gov/biblio/6752942
Leshchinsky, B., & Ambauen, S. (2015). Limit equilibrium and limit analysis: Comparison of benchmark slope stability problems. Journal of Geotechnical and Geoenvironmental Engineering, 141(10), 04015043. https://doi.org/10.1061/(asce)gt.1943-5606.0001347
Liu, L., Zhao, G., & Liang, W. (2023). Slope stability prediction using k-NN-based optimum-path forest approach. Mathematics, 11(14), 3071. https://doi.org/10.3390/math11143071
Liu, Z., Shao, J., Xu, W., Chen, H., & Zhang, Y. (2014). An Extreme Learning Machine Approach for Slope Stability Evaluation and Prediction. Natural Hazards (Dordrecht, Netherlands), 73(2), 787–804. https://doi.org/10.1007/s11069-014-1106-7
Mafi, R., Javankhoshdel, S., Cami, B., Jamshidi Chenari, R., & Gandomi, A. H. (2020). Surface altering optimisation in slope stability analysis with non-circular failure for random limit equilibrium method. Georisk Assessment and Management of Risk for Engineered Systems and Geohazards, 15, 1–27. https://doi.org/10.1080/17499518.2020.1771739
Maxwell, A. E., Sharma, M., Kite, J. S., Donaldson, K. A., Thompson, J. A., Bell, M. L., & Maynard, S. M. (2020). Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt. Remote Sensing, 12(3), 486. https://doi.org/10.3390/rs12030486
Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software (Barking, London, England: 1992), 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mishra, A. K., Ramteke, S. V., Sen, P., & Verma, A. K. (2018). Random Forest Tree Based Approach for Blast Design in Surface mine. Geotechnical and Geological Engineering, 36(3), 1647–1664. https://doi.org/10.1007/s10706-017-0420-8
Muro, C., Escobedo, R., Spector, L., & Coppinger, R. P. (2011). Wolf-pack (Canis Lupus) Hunting Strategies Emerge from Simple Rules in Computational Simulations. Behavioural Processes, 88(3), 192–197. https://doi.org/10.1016/j.beproc.2011.09.006
Nanehkaran, Y. A., Pusatli, T., Chengyong, J., Chen, J., Cemiloglu, A., Azarafza, M., & Derakhshani, R. (2022). Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis. Water (Switzerland), 14(22), 3743–3757. https://doi.org/10.3390/w14223743
Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017). Predicting Healthcare Trajectories from Medical Records: A Deep Learning Approach. Journal of Biomedical Informatics, 69, 218–229. https://doi.org/10.1016/j.jbi.2017.04.001
Pourkhosravani, A., & Kalantari, B. (2011). A review of current methods for slope stability evaluation. Electronic Journal of Geotechnical Engineering, 16, 1245–1254. https://www.academia.edu/download/52690111/A_Review_of_Current_Methods_for_Slope_Stability_Evaluation_.pdf
Rana, N., Latiff, M. S. A., Abdulhamid, S. M., & Chiroma, H. (2020). Whale Optimization Algorithm: A Systematic Review of Contemporary Applications, Modifications and Developments. Neural Computing & Applications, 32(20), 16245–16277. https://doi.org/10.1007/s00521-020-04849-z
Rigatti, S. J. (2017). Random Forest. Journal of Insurance Medicine (New York, N.Y.), 47(1), 31–39. https://doi.org/10.17849/insm-47-01-31-39.1
Samui, P. (2008). Slope Stability Analysis: A Support Vector Machine Approach. Environmental Geology, 56(2), 255–267. https://doi.org/10.1007/s00254-007-1161-4
Sinha, R., Gupta, A., Mishra, K., Tripathi, S., Nepal, S., Wahid, S. M., & Swarnkar, S. (2019). Basin-scale hydrology and sediment dynamics of the Kosi river in the Himalayan foreland. Journal of Hydrology, 570, 156–166. https://doi.org/10.1016/j.jhydrol.2018.12.051
Ullah, S., Khan, M. U., & Rehman, G. (2020). A Brief Review of the Slope Stability Analysis Methods. Geological Behavior, 4(2), 73–77. https://doi.org/10.26480/gbr.02.2020.73.77
Verma, D., Kainthola, A., Thareja, R., & Singh, T. N. (2013). Stability analysis of an open cut slope in Wardha valley coal field. Journal of the Geological Society of India, 81(6), 804–812. https://doi.org/10.1007/s12594-013-0105-8
Wei, W., Li, X., Liu, J., Zhou, Y., Li, L., & Zhou, J. (2021). Performance evaluation of hybrid WOA-SVR and HHO-SVR models with various kernels to predict factor of safety for circular failure slope. Applied Sciences (Basel, Switzerland), 11(4), 1922. https://doi.org/10.3390/app11041922
Zhu, T. (2020). Analysis on the Applicability of the Random Forest. Journal of Physics. Conference Series, 1607(1), 012123. https://doi.org/10.1088/1742-6596/1607/1/012123.