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The site-specific selection of the infiltration model based on the global dataset and random forest algorithm
Vadose Zone Journal ( IF 2.8 ) Pub Date : 2021-05-20 , DOI: 10.1002/vzj2.20125
Seongyun Kim 1, 2 , Gülay Karahan 3 , Manan Sharma 1 , Yakov Pachepsky 1
Affiliation  

The selection of the infiltration equation is important in various water research and management fields. Different infiltration equations were found to perform better than others in previous studies. Our objective was to use the Soil Water Infiltration Global (SWIG) database for (a) evaluating and comparing infiltration equations and (b) using the random forest algorithm to investigate how soil properties, land use, and infiltration measurement methods influence the infiltration equation selection. The performance of six equations (Horton, Mezencev, Collis-George, Green–Ampt, Philip, and Swartzendruber) was characterized by the Akaike information criterion obtained after fitting them to 4,830 cumulative infiltration datasets from the SWIG database. Then, the random forest algorithm was applied to predict the best infiltration equation using soil texture class, organic matter content, bulk density, saturated hydraulic conductivity, land use, and infiltration measurement method as inputs. The Horton, Mezencev, and Collis-George models were the best in 36, 24, and 12% of cases, respectively. Swartzendruber, Philip, and Green–Ampt were the best in 11, 10, and 7% cases, respectively. The different error of predicting the best infiltration equation was observed in different parts of input variable space. The infiltration method was by far the most important predictor for a model being the best across the whole database, followed by the organic C content and land use type. Organic C content and land use type were the most important predictors for the tension infiltrometer datasets. The SWIG database presents the opportunity to forecast which infiltration equation will work best in site-specific conditions.

中文翻译:

基于全局数据集和随机森林算法的入渗模型定点选择

入渗方程的选择在各种水研究和管理领域都很重要。在以前的研究中发现不同的渗透方程比其他渗透方程表现更好。我们的目标是使用土壤水渗透全球 (SWIG) 数据库 (a) 评估和比较渗透方程和 (b) 使用随机森林算法研究土壤特性、土地利用和渗透测量方法如何影响渗透方程选择. 六个方程(Horton、Mezencev、Collis-George、Green-Ampt、Philip 和 Swartzendruber)的性能由将它们拟合到 SWIG 数据库中的 4,830 个累积渗透数据集后获得的 Akaike 信息标准进行表征。然后,以土壤质地类别、有机质含量、容重、饱和导水率、土地利用和入渗测量方法为输入,应用随机森林算法预测最佳入渗方程。Horton、Mezencev 和 Collis-George 模型分别在 36%、24% 和 12% 的案例中表现最好。Swartzendruber、Philip 和 Green-Ampt 分别在 11%、10% 和 7% 的案例中表现最好。在输入变量空间的不同部分观察到预测最佳入渗方程的不同误差。到目前为止,渗透方法是整个数据库中最好的模型的最重要预测因子,其次是有机碳含量和土地利用类型。有机碳含量和土地利用类型是张力渗透计数据集最重要的预测因素。
更新日期:2021-06-08
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