Abstract—

In this research, six samples of valid and widely soil-water retention curve (SWRC) estimation models, including van Genuchten, Brooks and Corey, Fredlund and Xing, Durner, Kosugi, and Seki were studied. To realize this approach, in the first step, the accuracy of each model was calculated using ten statistical benchmarks, and then the numbers obtained from the fitting accuracy were standardized based on each benchmark by the standard fuzzy method so that all of them had a similar scale. Finally, with the sum of the fuzzy standardized values, an index for each model was obtained as a new index called the Best-Fit Model (BFM) index, which was the basis for comparing and evaluating the overall accuracy of the models in each soil texture. Accordingly, if the BFM index is more extensive and closer to 10, the model is more fitted and gets a higher rating. The superiority of this method to other similar studies is that here, as in the multi-criteria evaluation methods, it can simultaneously assess in terms of different statistical benchmarks and ranking the models according to the diversity in the reaction of each to various benchmarks provided. The results showed that Brooks and Corey model with the lowest and the Durner model with the highest BFM and rank among other models in most soil textures are considered as the weakest and as the most suitable model from the overall accuracy viewpoint of fitting based on the approach used throughout this study, respectively.

中文翻译：

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利用统计基准和模糊方法比较和评价六个土壤水分保持模型总精度的新方法

摘要-

在这项研究中，研究了六个有效且广泛的土壤水分保留曲线（SWRC）估计模型样本，包括van Genuchten，Brooks和Corey，Fredlund和Xing，Durner，Kosugi和Seki。为了实现这种方法，第一步是使用十个统计基准来计算每个模型的准确性，然后使用标准模糊方法基于每个基准对从拟合精度中获得的数字进行标准化，以使所有样本的相似性都相似。规模。最后，利用模糊标准化值的总和，获得每个模型的指标，作为新的指标，称为最佳拟合模型（BFM）指标，这是比较和评估每种土壤中模型总体准确性的基础质地。因此，如果BFM指数更广泛且接近10，该模型更适合并获得更高的评分。这种方法相对于其他类似研究的优势在于，与多标准评估方法一样，它可以同时根据不同的统计基准进行评估，并根据每种对提供的各种基准的反应的多样性对模型进行排名。结果表明，在大多数土壤质地中，从最低的Brooks和Corey模型以及最高的BFM和等级的Durner模型到其他土壤模型，从基于该方法的整体拟合精度来看，被认为是最弱和最合适的模型。在整个研究过程中分别使用。它可以同时根据不同的统计基准进行评估，并根据每种对提供的各种基准的反应的多样性对模型进行排名。结果表明，在大多数土壤质地中，从最低的Brooks和Corey模型以及最高的BFM和等级的Durner模型到其他土壤模型，从基于该方法的整体拟合精度来看，被认为是最弱和最合适的模型。在整个研究过程中分别使用。它可以同时根据不同的统计基准进行评估，并根据每种对提供的各种基准的反应的多样性对模型进行排名。结果表明，在大多数土壤质地中，从最低的Brooks和Corey模型以及最高的BFM和等级的Durner模型到其他土壤模型，从基于该方法的整体拟合精度来看，被认为是最弱和最合适的模型。在整个研究过程中分别使用。