Table 17 Comparison of model UBC7 and available models in the literature.

S. No.	Input variables	Data	Models	Test R	Reference
1	L, D, PI, Su, qv, Qi, T	169	GEP	0.9000	¹⁸
2	D, L, S, Ed, At, Ab, SPT_NS, SPT_NB, Cs, Cb, ϕ_S, ϕ_B	95	GA_ANFIS	0.9620	¹⁹
3	LT, Clay, Silt, Sand, PT, WL, NF, D	95	ANN	0.9747	²⁷
4	D, T₁, T₂, T₃, P_T, NGE, P_TE, d, SPT_S, SPT_T	472	ODFP-LSSVR	0.9644	²⁹
5	D, L, Qi, T, Su	256	GP	0.9187	³¹
6	D, T₁, T₂, T₃, P_T, NGE, P_TE, d, SPT_S, SPT_T	472	GA_RF	0.9646	³³
7	L, D, SPT_S, SPT_T, fc, fy	75	SVM	0.9580	³⁵
8	L, D, Q_c, F_S	72	PSO_ANFIS_GMDH	0.9600	⁴⁹
9	L, D, SPT, H, HW	125	GBT	0.9171	⁵¹
10	L, SPT, H	125	GBT	0.9033	⁵¹
11	L, D, PS, HW, H	296	GPR	0.9165	⁶¹
12	D, T₁, T₂, T₃, P_T, NGE, P_TE, d, SPT_S, SPT_T	2314	RF	0.9306	⁶⁴
13	T₁, T₂, NGE, SPT_T	472	GA_DNN	0.9418	⁶⁵
14	L, D, Qi, T, Su, PI, qv	126	GA_G_RVM	0.9727	Present Study

*GA_G_SRVM is the genetic algorithm optimized Gaussian kernel-based RVM model UBC7.

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