China Institute of Water Resources and Hydropower Research Researchers Have Published New Study Findings on Algorithms (Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm): Algorithms

2023 NOV 15 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- Investigators publish new report on algorithms. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects.”

Funders for this research include National Key R&D Program of China; Chinese National Natural Science Foundation.

Our news editors obtained a quote from the research from China Institute of Water Resources and Hydropower Research: “Although the current rainfall monitoring system in China has a dense station network and high-precision rainfall data, the time series is short. In contrast, historical rainfall data have a longer sample time series but lower precision. This study introduced a PCA algorithm to reconstruct historical rainfall data. Based on the temporal and spatial characteristics of rainfall extracted from high-resolution rainfall data over the past decade, historical (6 h intervals) rainfall spatial data were reconstructed into high-resolution (1 h intervals) spatial data to satisfy the requirements of the urban flood risk analysis. The results showed that the average error between the reconstructed data and measured values in the high-value area was within 15% and in the low-value area was within 20%, representing decreases of approximately 65% and 40%, respectively, compared to traditional interpolation data.”

According to the news editors, the research concluded: “The reconstructed historical spatial rainfall data conformed to the temporal and spatial distribution characteristics of rainfall, improved the granularity of rainfall spatial data, and enabled the effective and reasonable extraction and summary of the fine temporal and spatial distribution characteristics of rainfall.”

For more information on this research see: Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm. Water, 2023,15(3596):3596. (Water - http://www.mdpi.com/journal/water/). The publisher for Water is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/w15203596.

Our news journalists report that additional information may be obtained by contacting Yuanyuan Liu, China Institute of Water Resources and Hydropower Research, Beijing 100038, People’s Republic of China. Additional authors for this research include Yesen Liu, Shu Liu, Hancheng Ren, Peinan Tian, Nana Yang.

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