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多数据源土壤传递函数模型在水分模拟中的不确定性

DOI: 10.6041/j.issn.1000-1298.2012.11.010, PP. 45-50

Keywords: 土壤传递函数,数据源,不确定性,土壤水分特征曲线

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Abstract:

基于前馈神经网络的传递函数模型,采用4种不同的数据源预测土壤水分特征曲线,并借助误差统计指标、Hydrus-1D水动力学模型对传递函数模型的预测性能及其应用不确定性进行了分析。结果表明,预测土壤水吸力1000、10000、15000cm对应含水率θ1000、θ10000、θ15000时,相对于仅有颗粒组成变量的传递函数模型,增加容重和θ60(水吸力60cm对应的含水率)传递函数模型的平均绝对误差降低了42.86%、23.87%、26.15%;增加θ60和θ15000模型预测θ100、θ10000、θ15000的平均绝对误差比仅包含θ60模型降低了8.67%、16.96%、15.95%。将模型预测的vanGenuchten参数应用于土壤水分模拟,增加θ60模型的平均绝对误差比以颗粒组成为输入变量模型的相应值降低了11.11%;相对于增加θ60的传递函数,额外再增加θ15000并未降低模型应用过程中的不确定性。

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