Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR028936 |
Selection of multiple donor gauges via Graphical Lasso for estimation of daily streamflow time series | |
German A. Villalba; Xu Liang; Yao Liang | |
2021-03-22 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | A fundamental challenge in estimations of daily streamflow time series at sites with incomplete records is how to effectively and efficiently select reference/donor gauges from an existing gauge network to infer the missing data. While research on estimating missing streamflow time series is not new, the existing approaches either use a single reference streamflow gauge or employ a set of ‘ad‐hoc’ reference gauges, leaving a systematic selection of reference gauges as a long‐standing open question. In this work, a novel method is introduced that facilitates systematical selection of multiple reference gauges from any given streamflow network. The idea is to mathematically characterize the network‐wise correlation structure of a streamflow network via graphical Markov modeling, and further transforms a dense network into a sparsely connected one. The resulted underlying sparse graph from the graphical model encodes conditional independence conditions among all reference gauges from the streamflow network, allowing determination of an optimum subset of the donor gauges. The sparsity is discovered by using the Graphical Lasso algorithm with an L1‐norm regularization parameter and a thresholding parameter. These two parameters are determined by a multi‐objective optimization process. Furthermore, the graphical modeling approach is employed to solve another open problem in gauge removal planning decision (e.g., due to operation budget constraints): which gauges to remove would statistically guarantee the least loss of information by estimations from the remaining gauges? Our graphical model‐based method is demonstrated with daily streamflow data from a network of 34 gauges over the Ohio River basin region. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/320949 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | German A. Villalba,Xu Liang,Yao Liang. Selection of multiple donor gauges via Graphical Lasso for estimation of daily streamflow time series[J]. Water Resources Research,2021. |
APA | German A. Villalba,Xu Liang,&Yao Liang.(2021).Selection of multiple donor gauges via Graphical Lasso for estimation of daily streamflow time series.Water Resources Research. |
MLA | German A. Villalba,et al."Selection of multiple donor gauges via Graphical Lasso for estimation of daily streamflow time series".Water Resources Research (2021). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论