Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets
文献类型：期刊论文
| 作者 | Xue Cunjin1,2; Liao Xiaohan3 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2017-05-01 |
| 卷号 | 6期号:5页码:15 |
| 关键词 | data mining marine spatial association pattern marine remote sensing products ENSO Pacific Ocean |
| ISSN号 | 2220-9964 |
| DOI | 10.3390/ijgi6050139 |
| 通讯作者 | Liao Xiaohan(liaoxh@igsnrr.ac.cn) |
| 英文摘要 | The ENSO (El Nino Southern Oscillation) is the dominant inter-annual climate signal on Earth, and its relationships with marine environments constitute a complex interrelated system. As traditional methods face great challenges in analyzing which, how and where marine parameters change when ENSO events occur, we propose an ENSO-oriented marine spatial association pattern (EOMSAP) mining algorithm for dealing with multiple long-term raster-formatted datasets. EOMSAP consists of four key steps. The first quantifies the abnormal variations of marine parameters into three levels using the mean-standard deviation criteria of time series; the second categorizes La Nina events, neutral conditions, or El Nino events using an ENSO index; then, the EOMSAP designs a linking-pruning-generating recursive loop to generate (m + 1)-candidate association patterns from an m-dimensional one by combining a user-specified support with a conditional support; and the fourth generates strong association patterns according to the user-specified evaluation indicators. To demonstrate the feasibility and efficiency of EOMSAP, we present two case studies with real remote sensing datasets from January 1998 to December 2012: one considers performance analysis relative to the ENSO-Apriori and Apriori methods; and the other identifies marine spatial association patterns within the Pacific Ocean. |
| WOS关键词 | SEA-SURFACE TEMPERATURE ; NORTH PACIFIC ; EARTH-SCIENCE ; VARIABILITY ; RULES ; CLIMATE ; LEVEL ; OCEAN |
| 资助项目 | State Key Laboratory of Resources and Environmental Information System ; National key research and development program of China[2016YFA0600304] ; National Natural Science Foundation of China[41671401] ; National Natural Science Foundation of China[41401439] ; National Natural Science Foundation of China[41371385] |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000404525500014 |
| 出版者 | MDPI AG |
| 资助机构 | State Key Laboratory of Resources and Environmental Information System ; National key research and development program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/63205]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liao Xiaohan |
| 作者单位 | 1.Key Lab Earth Observat, Beijing 100094, Peoples R China 2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xue Cunjin,Liao Xiaohan. Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(5):15. |
| APA | Xue Cunjin,&Liao Xiaohan.(2017).Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(5),15. |
| MLA | Xue Cunjin,et al."Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.5(2017):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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