A binary granular algorithm for spatiotemporal meteorological data mining

This paper introduces the binary granule into the algorithm in computing and mining meteorological data. By redefining the binary algorithm, matching operators, convergence operators, disjunction operators, shift algorithm, and the shielding granule are given based on the binary granule. The function of operators is also explained, then different operators are applied on specific computing methods according to various requirements. Based on the binary granule, the whole sequence match algorithm is put forward, thus space-time efficiency of computing methods can be increased. Based on the knowledge of drought and flood distributions in China, this paper transforms meteorological drought and flood data into event set in the corresponding space and time after they are pre-processed using Standardized Precipitation Index (SPI) algorithm. Made by binary granulation, this event set is changed into the binary granule drought and flood event sets in different spatiotemporal scales. Granule operators are then applied to the whole sequence match algorithm, which describes the measuring module and definition of the whole sequence similarity matching, lastly the match mining is carried out. The result shows that research and application of the algorithm can provide a new method for monitoring and predicting drought and flood events in studying regional and local climate similarity further.