Open AccessAgrometeorological Disaster Grading in Guangdong Province Based on Data Mining
Author(s) -
Danni Wang,
Shitai Bao,
Wang Chun-lin,
Chongyang Wang
Publication year - 2017
Publication title -
journal of disaster research
Language(s) - English
Resource type - Journals
eISSN - 1883-8030
pISSN - 1881-2473
DOI - 10.20965/jdr.2017.p0187
Subject(s) - association rule learning , grading (engineering) , cluster analysis , fuzzy logic , flooding (psychology) , apriori algorithm , data mining , raw data , computer science , warning system , meteorological disasters , geography , mining engineering , engineering , civil engineering , artificial intelligence , psychology , telecommunications , agriculture , archaeology , psychotherapist , programming language
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.