Application of Apriori Algorithm in Meteorological Disaster Information Mining

Abstract

With the progresses of meteorological informatization, large amounts of meteorological data were accumulated. For the better application of these data, this research introduced data mining and applied it to analysis meteorological disaster, which can extract implicit, and potential useful information from a large amount of data in order to guide agricultural production. Meteorological disaster data affecting tea production were analyzed based on Apriori algorithm in this research, and the strong association rules between meteorological factors and reduction rate of tea yield were found. The results showed that the reduction rate of tea yield is closely related to the daily minimum temperature in the germination stage. When the daily minimum temperature was less than 5 °C, the tea would be affected by the cold and freezing damage and the tea yield would decrease, the lower the temperature, the greater the cold and freezing damage.