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Lifting scheme for streamflow data in river networks

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  • Seoncheol Park
  • Hee‐Seok Oh

Abstract

This paper presents a new multiscale method for analysing water pollutant data located in river networks. The main idea of the proposed method is to adapt the conventional lifting scheme, reflecting the characteristics of streamflow data in the river network domain. Due to the complexity of the data domain structure, it is difficult to apply the lifting scheme to the streamflow data directly. To solve this problem, we propose a new lifting scheme algorithm for streamflow data that incorporates flow‐adaptive neighbourhood selection, flow proportional weight generation and flow‐length adaptive removal point selection. A nondecimated version of the proposed lifting scheme is also provided. The simulation study demonstrates that the proposed method successfully performs a multiscale analysis of streamflow data. Furthermore, we provide a real data analysis of water pollutant data observed on the Geum‐River basin compared to the existing smoothing method.

Suggested Citation

  • Seoncheol Park & Hee‐Seok Oh, 2022. "Lifting scheme for streamflow data in river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 467-490, March.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:2:p:467-490
    DOI: 10.1111/rssc.12542
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    References listed on IDEAS

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    1. David O'Donnell & Alastair Rushworth & Adrian W. Bowman & E. Marian Scott & Mark Hallard, 2014. "Flexible regression models over river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 47-63, January.
    2. Maarten Jansen & Guy P. Nason & B. W. Silverman, 2009. "Multiscale methods for data on graphs and irregular multidimensional situations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 97-125, January.
    3. Ver Hoef, Jay M. & Peterson, Erin E., 2010. "A Moving Average Approach for Spatial Statistical Models of Stream Networks," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 6-18.
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