Movement Pattern Recognition of Tropical Cyclone Using Spatial Data Mining

The North Atlantic region is always exposed to severe storms, which are considered one of the most severe natural hazards in the climate field, and every year they cause serious damage to the economic infrastructure and human casualties in the areas affected by this event. These storms can help in the analysis and crisis management plans of this hazard and land preparation. The development of data collection and data mining technologies enables a more detailed study of this phenomenon. This issue requires the use of simple and efficient methods to investigate the behavior and extract the pattern from the database of this phenomenon. In this research, using spatial statistics methods, the trend of changes in the movement of tropical storms in the North Atlantic Ocean and the identification of their governing patterns in the period of 1995-2015 have been analyzed. The obtained results confirm the cluster pattern governing this phenomenon and that the occurrence of storms are not random events and follow spatial and temporal patterns in the studied area. The pattern of storms has a cluster pattern with the maximum value of the average value of the nearest neighborhood of 0.74 and the minimum value of 0.47. Also, the value of the general Moran index, the highest and the lowest correlation and clustering were calculated in 2006 with an index number of 0.66 and 2009 with an index number of 0.12 respectively, and a map of clusters and non-clusters and hot spots was prepared. With a better understanding of the patterns governing the movement of storms, it is possible to reduce possible damages caused by storms. Based on this, as a suggestion for future research, it is possible to include the effect of other parameters such as temperature, water salinity, and atmospheric general circulation systems, which play a significant role in the distribution of the distribution of the occurrence of storms, in the modeling and data mining of storms and get results closer to reality. Finally, as a useful research, the results of clusters and hot spots can be used in predicting the movement of storms in the future.