Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm
Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. 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 rainfa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/20/3596 |
| _version_ | 1797572016051585024 |
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| author | Yuanyuan Liu Yesen Liu Shu Liu Hancheng Ren Peinan Tian Nana Yang |
| author_facet | Yuanyuan Liu Yesen Liu Shu Liu Hancheng Ren Peinan Tian Nana Yang |
| author_sort | Yuanyuan Liu |
| collection | DOAJ |
| description | Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. 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. 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. |
| first_indexed | 2024-03-10T20:48:39Z |
| format | Article |
| id | doaj.art-35cc37b256b745348465f92c73a6d8d1 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T20:48:39Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-35cc37b256b745348465f92c73a6d8d12023-11-19T18:30:00ZengMDPI AGWater2073-44412023-10-011520359610.3390/w15203596Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA AlgorithmYuanyuan Liu0Yesen Liu1Shu Liu2Hancheng Ren3Peinan Tian4Nana Yang5China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChina Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChina Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChina Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChina Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChina Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaScientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. 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. 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.https://www.mdpi.com/2073-4441/15/20/3596machine learningPCA algorithmspatiotemporal distribution of heavy rainfeature extractionlow-resolution reconstructionLuzhou |
| spellingShingle | Yuanyuan Liu Yesen Liu Shu Liu Hancheng Ren Peinan Tian Nana Yang Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm Water machine learning PCA algorithm spatiotemporal distribution of heavy rain feature extraction low-resolution reconstruction Luzhou |
| title | Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm |
| title_full | Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm |
| title_fullStr | Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm |
| title_full_unstemmed | Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm |
| title_short | Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm |
| title_sort | extraction of spatiotemporal distribution characteristics and spatiotemporal reconstruction of rainfall data by pca algorithm |
| topic | machine learning PCA algorithm spatiotemporal distribution of heavy rain feature extraction low-resolution reconstruction Luzhou |
| url | https://www.mdpi.com/2073-4441/15/20/3596 |
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