Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China
Detection of abrupt change is a key issue for understanding the facts and trends of climate change, but it is also a difficult task in practice. The Mann-Kendall (MK) test is commonly used for treating the issue, while the results are usually affected by the correlation and seasonal characters and s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-07-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/14/7/1274 |
| _version_ | 1811187203215196160 |
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| author | Zong-Li Li Yan-Fang Sang Zhong-Gen Wang |
| author_facet | Zong-Li Li Yan-Fang Sang Zhong-Gen Wang |
| author_sort | Zong-Li Li |
| collection | DOAJ |
| description | Detection of abrupt change is a key issue for understanding the facts and trends of climate change, but it is also a difficult task in practice. The Mann-Kendall (MK) test is commonly used for treating the issue, while the results are usually affected by the correlation and seasonal characters and sample size of series. This paper proposes a discrete wavelet entropy-aided approach for abrupt change detection, with the temperature analyses in the Haihe River Basin (HRB) as an example. The results show some obviously abrupt temperature changes in the study area in the 1960s–1990s. The MK test results do not reflect those abrupt temperature changes after the 1980s. Comparatively, the proposed approach can detect all main abrupt temperature changes in HRB, so it is more effective than the MK test. Differing from the MK test which only considers series’ value order or the conventional entropy which mainly considers series’ statistical random characters, the proposed approach is to describe the complexity and disorderliness of series using wavelet entropy theories, and it can fairly consider series’ composition and characteristics under different scales, so the results can more accurately reflect not only the abrupt changes, but also the complexity variation of a series over time. However, since it is based on the entropy theories, the series analyzed must have big sample size enough and the sampling rates being smaller than the concerned scale for the accurate computation of entropy values. |
| first_indexed | 2024-04-11T13:58:09Z |
| format | Article |
| id | doaj.art-895f3f9f04244de1befa4ea25331a450 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:58:09Z |
| publishDate | 2012-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-895f3f9f04244de1befa4ea25331a4502022-12-22T04:20:11ZengMDPI AGEntropy1099-43002012-07-011471274128410.3390/e14071274Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, ChinaZong-Li LiYan-Fang SangZhong-Gen WangDetection of abrupt change is a key issue for understanding the facts and trends of climate change, but it is also a difficult task in practice. The Mann-Kendall (MK) test is commonly used for treating the issue, while the results are usually affected by the correlation and seasonal characters and sample size of series. This paper proposes a discrete wavelet entropy-aided approach for abrupt change detection, with the temperature analyses in the Haihe River Basin (HRB) as an example. The results show some obviously abrupt temperature changes in the study area in the 1960s–1990s. The MK test results do not reflect those abrupt temperature changes after the 1980s. Comparatively, the proposed approach can detect all main abrupt temperature changes in HRB, so it is more effective than the MK test. Differing from the MK test which only considers series’ value order or the conventional entropy which mainly considers series’ statistical random characters, the proposed approach is to describe the complexity and disorderliness of series using wavelet entropy theories, and it can fairly consider series’ composition and characteristics under different scales, so the results can more accurately reflect not only the abrupt changes, but also the complexity variation of a series over time. However, since it is based on the entropy theories, the series analyzed must have big sample size enough and the sampling rates being smaller than the concerned scale for the accurate computation of entropy values.http://www.mdpi.com/1099-4300/14/7/1274climate changetemperaturetime series analysisabrupt changewaveletinformation entropyHaihe River Basin |
| spellingShingle | Zong-Li Li Yan-Fang Sang Zhong-Gen Wang Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China Entropy climate change temperature time series analysis abrupt change wavelet information entropy Haihe River Basin |
| title | Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China |
| title_full | Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China |
| title_fullStr | Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China |
| title_full_unstemmed | Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China |
| title_short | Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China |
| title_sort | discrete wavelet entropy aided detection of abrupt change a case study in the haihe river basin china |
| topic | climate change temperature time series analysis abrupt change wavelet information entropy Haihe River Basin |
| url | http://www.mdpi.com/1099-4300/14/7/1274 |
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