Near-saturated hydraulic properties are the key parameters for water transport models in the unsaturated zone and essential for management practices. This study was conducted to compare efficacy of multiple linear regression (MLR) and hybrid method of genetic algorithm with artificial neural network (GA-ANN) for prediction of near-saturated soil hydraulic properties in Moghan plain, north-western Iran. The results of MLR analysis indicated that this method had low potential to predict near-saturated soil hydraulic properties in the study area, which only could explain 14–38% of variability in the studied properties. Otherwise, GA-ANN was much higher powerful that could explain about 35–80% of total variability in the mentioned properties in the study area. The results of sensitivity analysis suggest that soil particle size distribution, organic matter, electrical conductivity and relative bulk density were the most crucial with variety of priorities for explaining variability of the near-saturated soil hydraulic properties in the study area in the semiarid region. In overall, it was concluded that application of intelligent system using the easily available soil properties as predictors could provide reliable estimates of near-saturated soil hydraulic properties at the filed scale.

近饱和水力特性是非饱和区输水模型的关键参数，对于管理实践至关重要。进行了这项研究，以比较多元线性回归（MLR）和遗传算法与人工神经网络（GA-ANN）的混合方法预测伊朗西北部莫汉平原近饱和土壤水力特性的功效。MLR分析的结果表明，该方法在预测研究区域的近饱和土壤水力特性方面具有较低的潜力，这只能解释所研究特性的14％至38％的变异性。否则，GA-ANN的功能要强大得多，可以解释研究区域中提到的属性的总变异性的35-80％。敏感性分析的结果表明，土壤粒径分布，在解释半干旱地区研究区近饱和土壤水力特性的变化时，有机物，电导率和相对堆积密度是最关键的，具有各种优先级。总的来说，得出的结论是，使用易于获得的土壤性质作为预测因子的智能系统的应用可以在领域内提供对近饱和土壤水力学性质的可靠估计。