Table 2.

Provides an overview of several machine learning models discussed in the literature review. The bold letters represent the most optimal soft computing model in the research.

Authors	AI technique	Number of datapoints	Details of input variables		R2 test
			Variables	No.
Nguyen et al.20	SSO-ELM	216	B/S, DI, ST, MC, PF, HD, RQD, N, SD	9	0.91
Zhang et al.21	RF, CART, CHAID	102	DI, MC	2	0.94
Zhou et al.22	RF	102	DI, MC, PF, B, S, HD	6	0.93
Huang et al.23	FA-ANN	88	DI, MC, B, S, N	5	0.91
Zhou et al.24	GEP-MC	102	B/S, DI, ST, MC, PF, HD	6	0.91
Nguyen et al.25	SVR-GA	125	DI, MC	2	0.99
Nguyen et al.26	HKM-CA	136	DI, MC	2	0.99
Armaghani et al.27	ICA	73	B, S, ST, D, HD, PF, MC, DI	8	0.95
Hasanipanah et al.28	CART	86	DI, MC	2	0.95
Ghoraba et al.29	ANN, ANFIS	115	DI, MC	2	0.95
Shirani Faradonbeh et al.30	GEP	102	DI, MC	2	0.88
Hajihassani et al.31	ICA-ANN	95	MC, DI, TC	3	0.98
Hajihassani et al.32	PSO-ANN	88	BS, ST, PF, MC, DI, Vp, E	7	0.89
Hasanipanah et al.33	SVM	80	RD, E, UCS, TS, Js, B, S, HD/B, SC, ST, DPR, DI	12	0.96
Armaghani et al.34	ANFIS	109	BS, MC, HD, ST, SD, DI, PF, RQD	8	0.97
Armaghani et al.35	PSO-ANN	44	B, S, ST, N, MC, DI	6	0.94
Mohamadnejad et al.36	SVM, ANN	37	DI, MC	2	0.89
Monjezi et al.37	ANN	182	CD, DI, ST, HD	4	0.95
Mohamed38	ANN, FIS	162	DI, MC	2	0.94
Fişne et al.39	FIS	33	DI, MC	2	0.92
Iphar et al.40	ANFIS	42	DI, CD	2	0.99