Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Table 5

Best subset regression analysis for irrigation water quality indices input combinations.

Indices	Variables	MSE	R^2	Adjusted R^2	Mallows’ Cp	Akaike’s AIC	Schwarz’s SBC	Amemiya’s PC
SSP	Ca/Mg/Na	32.768	0.781	0.778	4.609	666.951	679.939	0.226
MH	EC (dS/m)/TDS/SO4/Ca/Mg	49.492	0.755	0.748	6.966	747.248	766.730	0.258
SAR	EC (dS/m)/TDS/SO4/Cl/Ca/Mg/Na/K	0.041	0.971	0.970	8.863	−599.143	−569.920	0.031
PI	pH/CO3(meq/l)/HCO3/SO4/Cl/Ca/Mg/Na/K	52.228	0.793	0.782	9.957	761.294	793.764	0.228
KR	pH/EC (dS/m)/TDS/CO3 (meq/l)/SO4/Cl/Ca/Mg/Na/K	0.015	0.883	0.876	10.426	−781.725	−746.008	0.130
%Na	pH/EC (dS/m)/CO3 (meq/l)/HCO3/SO4/Cl/Ca/Mg/Na/K	28.660	0.801	0.790	10.004	648.215	683.933	0.221