## 7 Conclusions and Summary  
The UCS is a parameter of great importance for designing geotechnical and mining works such as tunneling, underground excavation, rock slope stability, and dam construction. Therefore, five AI models (ELM, KELM, KELM-GWO, DELM, and BPNN) and an empirical equation were proposed to predict UCS based on the 271 rock samples. The results showed that the KELM-GWO model was the best model for predicting UCS with the best performance indices in both the training (RMSE: 17.2176; R\({}^{2}\): 0.8846; MAE: 12.0577; U\({}_{1}\): 0.0798; U\({}_{2}\): 0.0259; VAF: 88.4566%) and testing phases (RMSE: 14.2176; R\({}^{2}\): 0.9152; MAE: 11.4315; U\({}_{1}\): 0.0706; U\({}_{2}\): 0.0259; VAF: 91.5207%). It is also verified that GWO is an effective algorithm to improve the prediction performance of the KELM model. The porosity is the most important parameter for predicting UCS by using the MIV technique; the remaining order of importance for the others is point load strength of 10.74, Schmidt hardness rebound number of 9.50, and P-wave velocity of 8.47. This paper highlights the performance advantages of hybrid algorithms and the use of integrated large databases, but the parameter diversity considered in this paper limits the prediction performance to a certain extent. We believe that on the premise of fusing more reference data, increasing effective input parameters as much as possible is helpful to improve the predictive performance of the hybrid model.  
Conceptualization: [PERSON] and D.D.; methodology: [PERSON] and J.Z.; Investigation: [PERSON], [PERSON] and [PERSON]; Writing--original draft preparation: [PERSON]; Writing--review and editing: [PERSON], [PERSON] and [PERSON]; Visualization: [PERSON]; Funding acquisition: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 13: The distribution of error for various input variables with KELM-GWO model: (**a**) PLS; (**b**) P\({}_{n}\); (**c**) V\({}_{p}\); (**d**) SHR.  
**Funding:** This research is partially supported by the National Natural Science Foundation Project of China (42177164), the Distinguished Youth Science Foundation of Hunan Province of China (2022 JJ10073), and the Innovation-Driven Project of Central South University (2020 CX040). The first author was funded by the China Scholarship Council (Grant No. 202106370038).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this study are from published research: [PERSON] et al. [20] ([[https://doi.org/10.1016/S1674-5264](https://doi.org/10.1016/S1674-5264)]([https://doi.org/10.1016/S1674-5264](https://doi.org/10.1016/S1674-5264))(09)60158-7, accessed on 1 May 2022), [PERSON] et al. [69] ([[https://doi.org/10.1007/s12517-015-2057-3](https://doi.org/10.1007/s12517-015-2057-3)]([https://doi.org/10.1007/s12517-015-2057-3](https://doi.org/10.1007/s12517-015-2057-3)), accessed on 1 May 2022), and [PERSON] et al. [34] ([[https://doi.org/10.1016/j.trgeo.2020.100499](https://doi.org/10.1016/j.trgeo.2020.100499)]([https://doi.org/10.1016/j.trgeo.2020.100499](https://doi.org/10.1016/j.trgeo.2020.100499)), accessed on 2 May 2022).  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.