| 海陆气候事件关联规则挖掘方法 |
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| 引用本文： | 石岩,邓敏,刘启亮,杨文涛. 海陆气候事件关联规则挖掘方法[J]. 地球信息科学学报, 2014, 16(2): 182-190. DOI: 10.3724/SP.J.1047.2014.00182 |
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| 作者姓名： | 石岩 邓敏 刘启亮 杨文涛 |
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| 作者单位： | 1. 中南大学地理信息系, 长沙 410083;2. 香港理工大学土地测量与地理信息资讯学系, 香港红磡 999077 |
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| 基金项目： | 教育部新世纪优秀人才资助计划(NECT-10-0831);高等学校博士学科点专项科研基金项目(20110162110056);江苏省资源环境重点实验室开放基金项目(JS201101);现代工程测量国家测绘地理信息局重点实验室开放基金项目(TJES1102) |
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| 摘 要： | 近年来，异常气候事件的频发对人类的生活环境和经济发展带来严重负影响。气象学家研究表明：海洋气候异常对陆地气候异常事件的发生具有重要的诱发作用，因此，对海陆气候间的内在关联机制进行深入挖掘具有重要研究价值。本文提出了一种关联规则挖掘方法，以探索单一海洋气候指数与陆地异常气候事件间存在的关联。首先，针对陆地气候要素，采用顾及空间邻近关系的层次聚类方法进行有效气候分区，通过对各层分区结果进行相关统计分析得到有效的各区域气候序列；然后，进行顾及多重约束进行时序关联规则挖掘，以探索海陆气候要素间的关联机制；最后，通过实际算例分析得到的各气候指数与我国陆地区域异常降水事件间的关联机制结果，与实际情况高度吻合。
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| 关 键 词： | 气候指数 异常气候事件 气候分区 时序关联规则挖掘 |
| 收稿时间： | 2013-06-08 |
Discovering Sequential Association Rules between Single Ocean Climate Index and Land Abnormal Climate Events |
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| SHI Yan,DENG Min,LIU Qiliang,YANG Wentao. Discovering Sequential Association Rules between Single Ocean Climate Index and Land Abnormal Climate Events[J]. Geo-information Science, 2014, 16(2): 182-190. DOI: 10.3724/SP.J.1047.2014.00182 |
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| Authors: | SHI Yan DENG Min LIU Qiliang YANG Wentao |
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| Affiliation: | 1. Department of Geo-informatics, Central South University, Changsha 410083, China;2. Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China |
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| Abstract: | With the frequent occurrence of abnormal climatic events in recent years, social economic and people’ s life are impacted more and more seriously. Meteorologists have found that ocean climate has an effect on land climate, such that the EI NINO can lead abnormal precipitation events on some land regions. Therefore, it is very critical to study the associations between ocean and land climate factors. At present, some researchers have done a series of work about this aspect and several representative methods have been proposed. The eigenvalue statis-tics and traditional sequential association rules mining are two main methods. However, the former is sensitive to noise and not suitable for huge amounts of data, while the latter dose not fully consider the correlation and multi-scale properties hidden in the climate time series data. In view of this, a method based on multi-constraints is proposed to discover sequential association rules between individual ocean and land climate factors in this pa-per. First, we took both time correlation and spatial correlation into account and a hierarchical clustering method with the consideration of spatial proximity is employed to find climate zones for the land climate factor. In this way, we not only preserve the effective information in the data, but also make the raw data simpler by removing time correlation and spatial correlation. Second, the land and ocean climate sequences are discretized based on domain knowledge and a series of events are also extracted. These are further used to construct the transactions mining table. Finally, a new method, which utilizes multiple constraints, is developed to mine sequential associa-tion rules. We only focus on the associations between ocean climate indices and abnormal land climate events, such as flood and drought. As a matter of fact, we need the frequent rules which can describe a law to a certain extent. A practical example is used to explore the relationships between each climate index and unusual precipita-tion events in China, and the results obtained are very consistent with the actual situation. This to a large degree illustrates that the method proposed in this paper is rational. In addition, we also gain some unknown knowledge that can provide some information for meteorologists. Based on the information, meteorologists can study the in-ternal mechanism deeply. Also, the information can guide the government to make related policy decisions. In summary, the method in this paper takes the spatial correlation, time correlation and multi-scale characteristics in-to account effectively, while considers multi-constraint to deal with climate problems more accurately. By experi-ments, it is proved that our method is correct and valid. |
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| Keywords: | climate indices unusual climate events climate zone sequential association rules mining |
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