Discovering Sequential Association Rules between Single Ocean Climate Index and Land Abnormal Climate Events

  • 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

Received date: 2013-06-08

  Revised date: 2013-07-06

  Online published: 2014-03-10


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 statistics 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 paper. 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 association 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 precipitation 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 internal 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 into account effectively, while considers multi-constraint to deal with climate problems more accurately. By experiments, it is proved that our method is correct and valid.

Cite this article

SHI Yan, DENG Min, LIU Qiliang, YANG Wentao . Discovering Sequential Association Rules between Single Ocean Climate Index and Land Abnormal Climate Events[J]. Journal of Geo-information Science, 2014 , 16(2) : 182 -190 . DOI: 10.3724/SP.J.1047.2014.00182


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