Abstract

Direct measurement of the least limiting water range (LLWR) is costly and time-consuming. In this study, genetic algorithm-based neural network (ANN-GA), artificial neural network (ANN) and stepwise multivariate regression (SMR) were used to estimate the LLWR of soil using easily measurable soil properties in the Khanmirza Plain. Then, depending on the location of each area, a total of 250 points were randomly identified as approximate sampling sites. Results showed that the accuracy of the SMR model with the percentage of clay, organic carbon and fine sand had a coefficient of determination of 0.42. The ANN-GA and ANN models with the highest coefficient of determination (R² = 0.98) and mean square error (MAE = 0.0538) were suitable for estimating the least limiting water range. Therefore, the efficiency of models showed that the ANN and ANN-GA predicted the LLWR more accurate compared to the SMR and their results were close to the measured ones.

中文翻译：

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使用方便可测量的土壤特性估算最小限水范围的智能模型的评估

摘要

直接测量最低限制水位（LLWR）既费钱又费时。在这项研究中，使用基于遗传算法的神经网络（ANN-GA），人工神经网络（ANN）和逐步多元回归（SMR）来评估汉米尔扎平原土壤的LLWR，方法是使用易于测量的土壤属性。然后，根据每个区域的位置，总共将250个点随机标识为近似采样点。结果表明，含粘土，有机碳和细砂百分比的SMR模型的测定系数为0.42。测定系数最高的ANN-GA和ANN模型（R ²= 0.98）和均方误差（MAE = 0.0538）适合估算最小极限水位范围。因此，模型的有效性表明，与SMR相比，ANN和ANN-GA预测LLWR更为准确，其结果与实测值相近。