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Principal component analysis for river network data: Use of spatiotemporal correlation and heterogeneous covariance structure

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  • Kyusoon Kim
  • Hee‐Seok Oh
  • Minsu Park

Abstract

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.

Suggested Citation

  • Kyusoon Kim & Hee‐Seok Oh & Minsu Park, 2023. "Principal component analysis for river network data: Use of spatiotemporal correlation and heterogeneous covariance structure," Environmetrics, John Wiley & Sons, Ltd., vol. 34(4), June.
  • Handle: RePEc:wly:envmet:v:34:y:2023:i:4:n:e2753
    DOI: 10.1002/env.2753
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    References listed on IDEAS

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    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. W. J. Krzanowski, 1984. "Principal Component Analysis in the Presence of Group Structure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(2), pages 164-168, June.
    3. Trendafilov, Nickolay T., 2010. "Stepwise estimation of common principal components," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3446-3457, December.
    4. E. Andrés Houseman, 2005. "A robust regression model for a first‐order autoregressive time series with unequal spacing: application to water monitoring," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(4), pages 769-780, August.
    5. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
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