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Principal component analysis for river network data: Use of spatiotemporal correlation and heterogeneous covariance structure
Environmetrics ( IF 1.7 ) Pub Date : 2022-08-12 , DOI: 10.1002/env.2753
Kyusoon Kim 1 , Hee‐Seok Oh 1 , Minsu Park 2
Affiliation  

Spatiotemporal measurements observed through river networks have two distinct characteristics: a spatiotemporal correlation under the flow-connected structure and the existence of heterogeneous covariances, which require a careful approach to implement principal component analysis (PCA). This paper focuses on developing a PCA method to reflect the unique characteristics of river networks. We propose a novel method combining flow-directed PCA and geographically weighted PCA for the domain of river networks. The strengths of our approach are that it can (i) reduce dimensionality for streamflow data while effectively removing correlation among them and (ii) identify the group structure of data. It is possible to find essential patterns and sources of variation that may not be disclosed due to the attributes of flow-connected networks. We apply the proposed method to the daily monitoring records of total organic carbon in the Geum River catchment area in South Korea. The results show that the proposed method successfully adjusts for the topological structure of the network and temporal correlation among observations while considering the spatial heterogeneity, enabling a more concrete understanding of monitoring networks.

中文翻译:

河网数据的主成分分析:时空相关性和异构协方差结构的使用

通过河网观察到的时空测量具有两个明显的特征:流量连接结构下的时空相关性和异质协方差的存在,这需要谨慎的方法来实施主成分分析 (PCA)。本文着重于开发一种 PCA 方法来反映河网的独特特征。我们针对河网领域提出了一种结合流导向 PCA 和地理加权 PCA 的新方法。我们的方法的优势在于它可以 (i) 降低流数据的维度,同时有效地消除它们之间的相关性,以及 (ii) 识别数据的组结构。有可能找到由于流连接网络的属性而可能未公开的基本模式和变化来源。我们将所提出的方法应用于韩国锦江流域总有机碳的每日监测记录。结果表明,所提出的方法在考虑空间异质性的同时成功地调整了网络的拓扑结构和观测值之间的时间相关性,从而能够更具体地理解监测网络。
更新日期:2022-08-12
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