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首页> 外文期刊>Soil Science Society of America Journal >Development of Pedotransfer Functions for Estimation of Soil Hydraulic Parameters using Support Vector Machines
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Development of Pedotransfer Functions for Estimation of Soil Hydraulic Parameters using Support Vector Machines

机译：支持向量机在土壤水力参数估算中的Pedotransfer函数开发

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Modeling flow in variably saturated porous media requires reliable estimates of the hydraulic parameters describing the soil water retention and hydraulic conductivity. These soil hydraulic properties can be measured using a wide variety of laboratory and field methods. Frequently, this proves to be an arduous task because of the high spatial and temporal variability of soil properties. In the last decade, researchers have shown a keen interest in developing a class of indirect approaches, called pedotransfer functions (PTFs), to overcome this problem. Pedotransfer functions predict soil hydraulic parameters using easily obtainable soil properties such as textural information, bulk density and/or few retention points. In this paper, we use a new methodology called Support Vector Machines (SVMs) to derive a new set of PTFs. Support vector machines represent a pattern recognition approach where the overall prediction error and complexity of the SVM structure are minimized simultaneously. We used the same database that was utilized to develop ROSETTA to generate the SVM-based PTFs. The performance of the SVM-based PTFs was analyzed using the coefficient of determination, root mean square error (RMSE) and mean error (ME). All soil hydraulic parameters estimated using the SVM-based PTFs showed improved confidence in the estimates when compared with the ROSETTA PTF program. Estimates of water contents and saturated hydraulic conductivities using the hydraulic parameters predicted by the SVM-based PTFs mostly improved compared with those obtained using the artificial neural network (ANN)-based ROSETTA. The RMSE for water contents decreased from 0.062 to 0.034 as more predictors were used, while the RMSE for the saturated hydraulic conductivity decreased from 0.716 to 0.552 (dimensionless log10 units). Similarly, the bias in the water contents estimated using the SVM-based PTF was reduced significantly compared with ROSETTA.
机译：对可变饱和多孔介质中的流量进行建模需要可靠的水力参数估算，以描述土壤保水率和水力传导率。这些土壤水力特性可以使用多种实验室和现场方法进行测量。通常，由于土壤特性的高时空变化，这被证明是一项艰巨的任务。在过去的十年中，研究人员对开发一种称为pedotransfer函数（PTF）的间接方法以克服这一问题表现出了浓厚的兴趣。 Pedotransfer函数使用容易获得的土壤特性（例如纹理信息，堆积密度和/或很少的保留点）来预测土壤水力参数。在本文中，我们使用一种称为支持向量机（SVM）的新方法来推导一组新的PTF。支持向量机代表一种模式识别方法，其中，同时将总体预测误差和SVM结构的复杂性降至最低。我们使用了用于开发ROSETTA的相同数据库来生成基于SVM的PTF。使用确定系数，均方根误差（RMSE）和均值误差（ME）分析了基于SVM的PTF的性能。与ROSETTA PTF程序相比，使用基于SVM的PTF估算的所有土壤水力参数对估算值的置信度更高。与使用基于人工神经网络（ANN）的ROSETTA所获得的水力参数相比，使用基于SVM的PTF预测的水力参数来估算水含量和饱和水导率的方法大为改进。随着使用更多的预测因素，水的RMSE从0.062降低至0.034，而饱和水力传导率的RMSE从0.716降低至0.552（无量纲log10单位）。同样，与ROSETTA相比，使用基于SVM的PTF估算的含水量偏差明显降低。

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