| The influence of extreme weather and climate events restricts the development of society and economy,and directly threatens the ecological environment that human beings depend on.Therefore,the governments of all countries attach great importance to it and become one of the important research directions of the Meteorological Science.In recent years,the research on the regional distribution of extreme climate events has gradually become a research hotspot.Traditionally,statistical methods are usually used to study and process the accumulated meteorological data,but with the increase of the index level of the data and the continuous expansion of the data dimension,it has become a trend and necessity to apply the data mining technology to the processing and representation of Meteorological Data.In this thesis,the Apriori algorithm of mining association rules in data mining technology is used to study the regional association pattern of extreme climate events in small areas.On this basis,the K-means clustering algorithm is used to identify and express the regional aggregation of time and space data in the extreme temperature events nationwide,thus providing a new idea and method for the study of extreme climate events.The main contents of this thesis are as follows:Firstly,the treatment methods of extreme temperature events at home and abroad are introduced,especially the development direction of meteorological data processing.Secondly,the small range spatial data of extreme temperature events are analyzed,and the Apriori algorithm is used to study the extreme high temperature events in Shandong province region.The results show that the regions involved in the correlation model of Shandong Province have obvious interrelation in space.Thirdly,the data processing capacity in the small area is extended to the large area of the country,and the K-means algorithm which can deal with big data set quickly and efficiently,is used for further research.The results show that the occurrence of extreme high temperature events is not only regional aggregation but also regional mobility.Finally,the full content in this paper is summarized,and the further development of data mining and other related technologies in meteorological research and application is prospected. |
