Improving Situational Awareness for Precursory Data Classification using Attribute Rough Set Reduction Approach

The task of modeling the distribution of a large number of earthquake events with frequent tremors detected prior to a main shock presents us unique challenges to model a robust classifier tool for rapid responses are needed in order to address victims. We have designed using a relational database for running a geophysical modeling application after connecting database record of all clusters of foreshock events from (1998-2010) for a complete catalog of seismicity analysis for the Himalayan basin. by Nath et al,2010. This paper develops a reduced rough set analysis method and implements this novel structure and reasoning process for foreshock cluster forecasting. In this study, we developed a reusable information technology infrastructure, called Efficient Machine Readable for Emergency Text Selection(EMRETS). The association and importance of precursory information in reference to earthquake rupture analysis is found out through attribute reduction based on rough set analysis. Secondly, find the importance of attributes through information entropy is a novel approach for high dimensional complex polynomial problems predominant in geo-physical research and prospecting. Thirdly, we discuss the reducible indiscernible matrix and decision rule generation for a particular set of geographical co-ordinates leading to the spatial discovery of future earthquake having prior foreshock. This paper proposes a framework for extracting, classifying, analyzing, and presenting semi-structured catalog data sources through feature representation and selection.