Application of rough set and decision tree for characterization of premonitory factors of low seismic activity

Abstract

This paper presents a machine learning approach to characterizing premonitory factors of earthquake. The characteristic asymmetric distribution of seismic events and sampling limitations make it difficult to apply the conventional statistical predictive techniques. The paper shows that inductive machine learning techniques such as rough set theory and decision tree (C4.5 algorithm) allows developing knowledge representation structure of seismic activity in term of meaningful decision rules involving premonitory descriptors such as space-time distribution of radon concentration and environmental variables. The both techniques identify significant premonitory variables and rank attributes using information theoretic measures, e.g., entropy and frequency of occurrence in reducts. The cross-validation based on ''leave-one-out'' method shows that although the overall predictive and discriminatory performance of decision tree is to some extent better than rough set, the difference is not statistically significant.