Research On Typhoon Path Classification Method Based On Hot Spot Region Motif Mining

Tropical cyclone is one of the most destructive natural disasters,which brings great disaster to human society every year.The big data mining technology in typhoon field is gradually rising in the continuous research of scholars.The machine learning method represented by clustering has effectively promoted the further development of typhoon field because of its advantages of simple use and convenient calculation.In the field of typhoon research,typhoon track is an important way to analyze the characteristics of typhoon,and it is also an important method to judge the influence area and scope of typhoon.In recent years,typhoon track clustering has attracted extensive attention in marine disaster prevention and mitigation.Because the typhoon track is a time series data,many scholars use time series clustering method to cluster the historical typhoon track data,and further carry out statistical analysis and mining rules on the clustered typhoon track cluster.The traditional time series clustering method has limited clustering accuracy and randomness,which can not better meet the operational needs of typhoon disaster warning.Therefore,this paper proposes an improved clustering algorithm and motif mining algorithm to study the optimal track data of typhoons in the Northwest Pacific from 1949 to 2018.The main research contents are as follows:(1)Traditional typhoon track classification mainly uses subjective identification and K-means clustering methods.There are some problems in these methods,such as the randomness caused by the traditional clustering algorithm principle,the intelligent problem of relying on artificial experience to set the model parameters,and the limitations of specific typhoon research.Aiming at the problems of traditional track classification,this paper proposes a hybrid clustering algorithm based on the density centroid,and uses the geographical distribution density as the dividing standard to cluster the geographical distribution of the typhoon generating point and landing point,and obtain hot spots.It effectively reduces the randomness of traditional clustering methods and improves the practicability of clustering algorithm;(2)Typhoon track data is a relatively special time series data,which is not only time-dependent,but also spatially relevant.In order to better use the spatial correlation of typhoon tracks for typhoon track classification.this paper proposes a typhoon track classification algorithm combined with hot area motif mining.From all the typhoon path data,the most frequent and representative typhoon path motif is mined,and the results are analyzed typhoon tracks are classified by similarity threshold.This method effectively mines the key features of spatial features of typhoon track,and realizes the adaptive track motif mining method without prior intervention and automatic selection.Finally,the classification results of typhoon tracks are analyzed from the aspects of season,intensity and life cycle,which further verifies the correctness and practicability of the classification method,and provides a certain reference for the research of typhoon field.