On The Laws Of MCSs Spatio-temporal Evolution Based On Spatial Data Mining

With the advancement of spatial information acquisition technology, modern data acquisition technology develops rapidly. Different types of spatial database and the data quantity that is relative with weather is increasing fast, but large amount of useful information is submerged in these data and can not got the best exploration and use. How to pick up the mode and model that is relative to disaster weather from these mass data and take them as the information and knowledge to forecast the happening of disaster weather become the researching emphasis, foreland and hotspot of remote sensing, geographical information system and meteorology.But due to the inherent characteristics of the multi data source, multi type, multi temporal and multi resolution, one data mining system to excavate all types of weather data is unrealistic. So, in order to excavate specific types of data, specific data mining system should be constructed. Aiming at the diversity of weather data types and the technique problems existing in the application of spatial data mining, this thesis has a research and resolves the following problems, with the background of the research of Mesoscale Convective Systems (MCSs) Spatio-temporal evolution rules in Tibetan Plateau using spatial data mining technology.1. How to extract the spatial features of MCSs in fit quantity and impersonal from the weather satellite infrared remote sensing image and construct specific MCSs spatial database?2. How to construct MCSs spatial data mining database.3. Seek efficient database reduction methods.4. MCSs spatial evolution rules mining and its visual expression of knowledge discovery.The data mining on MCSs in this thesis provides the research thought and methods for the study on moving and propagation of MCSs. The mining result is also helpful to forecast heavy rainfall in Yangtze River Basin with forecasting MCSs moving out to east from Tibetan Plateau.