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首页> 外文期刊>Applied Mathematical Modelling >Prediction of soil water retention curve using Bayesian updating from limited measurement data
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Prediction of soil water retention curve using Bayesian updating from limited measurement data

机译：利用有限测量数据的贝叶斯更新预测土壤保水曲线

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摘要

A soil water retention curve is one of the fundamental elements used to describe unsaturated soil. The accurate determination of soil water retention curve requires sufficient available information. However, the amount of measurement data is generally limited due to the restriction of time or test apparatus. As a result, it is a challenge to determine the soil water retention curve from limited measurement data. To address this problem, a Bayesian framework is proposed. In the Bayesian framework, Bayesian updating can be employed using the posterior distribution that is obtained by the Markov chain Monte Carlo sampling method with the Delayed Rejection Adaptive Metropolis algorithm. The parameters of soil water retention curve model are represented by the sample statistics of updating posterior distribution. A new updating algorithm based on Bayesian framework is proposed to predict the soil water retention curve using the ideal data and the limited measurement data of the granite residual soil and sand. The results show that the proposed prediction algorithm exhibits an excellent capability for more accurately determining the soil water retention curve with limited measured data. The uncertainty of updating parameters and the influence of the prior knowledge can be reduced. The converged results can be derived using the proposed prediction algorithm even if the prior knowledge is incomplete. (C) 2019 Elsevier Inc. All rights reserved.
机译：土壤保水曲线是用来描述非饱和土壤的基本要素之一。准确确定土壤保水曲线需要足够的可用信息。然而，由于时间或测试设备的限制，通常限制测量数据的量。结果，从有限的测量数据确定土壤保水曲线是一个挑战。为了解决这个问题，提出了一种贝叶斯框架。在贝叶斯框架中，可以使用通过马尔可夫链蒙特卡洛采样方法和延迟拒绝自适应大都会算法获得的后验分布进行贝叶斯更新。用更新后验分布的样本统计量表示土壤保水曲线模型的参数。提出了一种基于贝叶斯框架的更新算法，利用花岗岩残余土和砂的理想数据和有限测量数据，预测了土壤保水曲线。结果表明，所提出的预测算法在有限的测量数据下具有更准确地确定土壤保水曲线的优良能力。可以减少参数更新的不确定性和先验知识的影响。即使先验知识不完整，也可以使用提出的预测算法得出收敛结果。 （C）2019 Elsevier Inc.保留所有权利。

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