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Article

Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope

1
School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
2
Wanbao Mining Ltd., Xicheng District, Beijing 100053, China
*
Author to whom correspondence should be addressed.
Academic Editor: Rodolfo Dufo-López
Appl. Sci. 2021, 11(4), 1922; https://doi.org/10.3390/app11041922
Received: 21 December 2020 / Revised: 30 January 2021 / Accepted: 10 February 2021 / Published: 22 February 2021
(This article belongs to the Special Issue Heuristic Algorithms in Engineering and Applied Sciences)
To detect areas with the potential for landslides, slopes are routinely subjected to stability analyses. To this end, there is a need to adopt appropriate mitigation techniques. In general, the stability of slopes with circular failure mode is defined as the factor of safety (FOS). The literature includes a variety of numerical/analytical models proposed in different studies to compute the FOS values of slopes. However, the main challenge is to propose a model for solving a non-linear relationship between independent parameters (which have a great impact on slope stability) and FOS values of slopes. This creates a problem with a high level of complexity and with multiple variables. To resolve the problem, this study proposes a new hybrid intelligent model for FOS evaluation and analysis of slopes in two different phases: simulation and optimization. In the simulation phase, different support vector regression (SVR) kernels were built to predict FOS values. The results showed that the radius basis function (RBF) kernel produces more accurate performance prediction compared with the other applied kernels. The prediction accuracy of this kernel was obtained as coefficient of determination = 0.94, which indicates a high prediction capacity during the simulation phase. Then, in the optimization phase, the proposed SVR model was optimized through the use of two well-known techniques, namely, the whale optimization algorithm (WOA) and Harris hawks optimization (HHO), and the optimum input parameters were obtained. The optimal results confirmed that both optimization techniques are able to achieve a high value for FOS of slopes; however, the HHO shows a more powerful process in FOS maximization compared with the WOA technique. In addition, the developed model was also successfully validated using new data with nine data samples. View Full-Text
Keywords: slope stability; factor of safety; support vector regression; whale optimization algorithm; Harris hawks optimization slope stability; factor of safety; support vector regression; whale optimization algorithm; Harris hawks optimization
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MDPI and ACS Style

Wei, W.; Li, X.; Liu, J.; Zhou, Y.; Li, L.; Zhou, J. Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope. Appl. Sci. 2021, 11, 1922. https://doi.org/10.3390/app11041922

AMA Style

Wei W, Li X, Liu J, Zhou Y, Li L, Zhou J. Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope. Applied Sciences. 2021; 11(4):1922. https://doi.org/10.3390/app11041922

Chicago/Turabian Style

Wei, Wei, Xibing Li, Jingzhi Liu, Yaodong Zhou, Lu Li, and Jian Zhou. 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 11, no. 4: 1922. https://doi.org/10.3390/app11041922

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