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EVALUATION OF REGIONAL PEDO- TRANSFER FUNCTIONS BASED ON THE BP NEURAL NETWORKS

机译:基于BP神经网络的区域人际转移函数评价。

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The unsaturated soil hydraulic properties, including soil water retention curve and hydraulic conductivity, are the crucial input parameters for simulating soil water and solute transport through the unsaturated zone at regional scales, and are expensive to measure. These properties are frequently predicted with pedo-transfer functions (PTFs) using the routinely measured soil properties. 110 soil samples at 22 soil profiles from Jiefangzha Irrigation Scheme in the Hetao Irrigation District of Inner Mongolia, China were collected for the analysis of soil properties i.e. soil bulk density, soil texture, particle size distribution, organic content, and soil water retention curve (SWRC). The Brooks-Corey (BC) model and van Genuchten (VG) model were used to fit the measured SWRC data for each soil sample by using the RETC software. Pedo-transfer functions (PTFS), which describes relationship between the basic soil properties and the parameters of the BC and VG models, were then established with the artificial neural networks (ANN) model. It is found that the ANN model has better effect on the clay loam, loamy clay, loam soil and silty clay to simulate BC model. However, it has better effect on the loam soil, loamy clay and sandy clay to simulate VG model. So, we can draw the conclusion that the ANN model can conveniently establish PTFS between soil basic feature parameters and SWRC model and has reasonable precision. This will be a good method to estimate soil water characteristic curve model and soil hydraulic parameter in the regional soil water and salt movement simulation and water resources evaluation.
机译:非饱和土壤的水力特性,包括土壤保水曲线和水力传导率,是模拟土壤水和溶质在区域尺度上通过非饱和区运移的关键输入参数,并且测量起来很昂贵。这些特性通常使用常规测量的土壤特性通过脚踏传递函数(PTF)进行预测。收集了内蒙古河套灌区解放闸灌溉计划的22个土壤剖面的110个土壤样品,以分析土壤性质,例如土壤容重,土壤质地,粒径分布,有机物含量和土壤保水曲线( SWRC)。通过使用RETC软件,使用Brooks-Corey(BC)模型和van Genuchten(VG)模型拟合每个土壤样品的实测SWRC数据。然后使用人工神经网络(ANN)模型建立了Pedo传递函数(PTFS),该函数描述了基本土壤特性与BC和VG模型的参数之间的关系。结果表明,人工神经网络模型对黏土,壤土,壤土和粉质黏土具有更好的效果,可以模拟BC模型。然而,它对壤土,壤质粘土和砂质粘土具有更好的效果,以模拟VG模型。因此,我们可以得出这样的结论:神经网络模型可以方便地在土壤基本特征参数和SWRC模型之间建立PTFS,并且具有合理的精度。在区域土壤水盐运移模拟和水资源评价中,这是估算土壤水分特征曲线模型和土壤水力参数的好方法。

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