Agrometeorological Disaster Grading in Guangdong Province Based on Data Mining
Danni Wang*1, Shitai Bao*2,†, Chunlin Wang*3, and Chongyang Wang*4
*1Department of Resources and the Urban Planning, XinHua College of Sun Yat-sen University
721 Guang Shan 1st Road, Tian He District, Guangzhou 510520, China
*2The College of Natural Resources and Environment, South China Agriculture University
483 Wushan Road, TianHe District, Guangzhou 510642, China
*3Climate Center of Guangdong Province
68 Gongyeyilu Road, Zhicun, Dashi Street, Panyu District, Guangzhouv 511430, China
*4Guangzhou Institute of Geochemistry, Chinese Academy of Sciences
100 Xianlie Road, Yuexiu District, Guangzhou 510070, China
This study proposes a mining method for meteorological disaster grade rules from the raw data accumulated by meteorological stations using fuzzy association rules. Rules for grading agrometeorological disasters are created and successfully applied to a map. The intention is to mitigate such disasters by understanding their conditions. The procedure described uses the fuzzy c-means clustering algorithm and the Apriori algorithm to mine fuzzy association rules for high-temperature and flooding agrometeorological disasters in Guangdong province, China. In the proposed method, the clustering algorithm does not depend on the membership functions of domain experts. The results show that effective association rules for agrometeorological disasters can be obtained from meteorological data in the long term, even with a lack of prior knowledge. The rules obtained could be used to forecast the grade and region of such disasters in Guangdong province, thus contributing to agrometeorological disaster monitoring and early warning efforts.
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