Improving parameter priors for data‐scarce estimation problems
Improving parameter priors for data‐scarce estimation problems
复制标题
改进数据稀缺估计问题的参数先验
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
W. Buytaert
中科院分区：
文献类型：
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作者：
S. Almeida;N. Bulygina;N. McIntyre;Thorsten Wagener;W. Buytaert
未测量集水区的径流预测是水文学中一个反复出现的问题。概念模型通常通过定义一个可行的参数范围来校准，然后根据观察到的系统响应（例如，水流）调节参数集。在未测量的集水区，一些研究使用贝叶斯程序对区域化响应特征（如径流比或基流指数）进行了条件模型。在本技术说明中，模型参数估计实验（MOPEX）数据集用于探索关于先验分布的假设对模型性能的影响。特别是，一般的参数均匀先验假设是不合适的。这是因为参数上的均匀先验映射到倾斜的响应签名先验，从而抵消了从区域化中获得的有价值的信息。为了解决这个问题，我们测试了一种方法开发，该方法基于将参数上的均匀先验初始转换为映射到均匀响应签名分布的先验。我们证明了这种方法有助于改进响应特征的估计。
Runoff prediction in ungauged catchments is a recurrent problem in hydrology. Conceptual models are usually calibrated by defining a feasible parameter range and then conditioning parameter sets on observed system responses, e.g., streamflow. In ungauged catchments, several studies condition models on regionalized response signatures, such as runoff ratio or base flow index, using a Bayesian procedure. In this technical note, the Model Parameter Estimation Experiment (MOPEX) data set is used to explore the impact on model performance of assumptions made about the prior distribution. In particular, the common assumption of uniform prior on parameters is shown to be unsuitable. This is because the uniform prior on parameters maps onto skewed response signature priors that can counteract the valuable information gained from the regionalization. To address this issue, we test a methodological development based on an initial transformation of the uniform prior on parameters into a prior that maps to a uniform response signature distribution. We demonstrate that this method contributes to improved estimation of the response signatures.
影响因子：
5.4
作者：
N. Bulygina;C. Ballard;N. McIntyre;G. O'Donnell;H. Wheater
通讯作者：
N. Bulygina;C. Ballard;N. McIntyre;G. O'Donnell;H. Wheater