期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
卷 71, 期 2, 页码 467-490出版社
WILEY
DOI: 10.1111/rssc.12542
关键词
lifting scheme; river network; smoothing; spatial adaptation; spatial modelling; streamflow data
资金
- Canada First Research Excellence Fund (Global Water Futures: Solutions to Water Threats in an Era of Global Change, Climate-Related Precipitation Extremes project)
- Pacific Climate Impacts Consortium
- Canadian Statistical Sciences Institute
- National Research Foundation of Korea (NRF) - Korea government [2018R1D1A1B07042933, 2020R1A4A1018207]
- National Research Foundation of Korea [2018R1D1A1B07042933, 2020R1A4A1018207] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper introduces a new multiscale method for analysing water pollutant data in river networks. By adapting the conventional lifting scheme, the method successfully performs multiscale analysis of streamflow data.
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.
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