Earthquake Prediction Using Data Mining

Data mining consists of evolving set of techniques that can be used to extract valuable information and knowledge from massive volumes of data. Data mining research& tools have focused on commercial sector applications. Only a fewer data mining research have focused on scientific data. This paper aims at further data mining study on scientific data. This paper highlights the data mining techniques applied to mine for surface changes over time (e. g. Earthquake rupture). The data mining techniques help researchers to predict the changes in the intensity of volcanoes. This paper uses predictive statistical models that can be applied to areas such as seismic activity, the spreading of fire. The basic problem in this class of systems is unobservable dynamics with respect to earthquakes. The space-time patterns associated with time, location and magnitude of the sudden events from the force threshold are observable. This paper highlights the observable space time earthquake patterns from unobservable dynamics using data mining techniques, pattern recognition and ensemble forecasting. Thus this paper gives insight on how data mining can be applied in finding the consequences of earthquakes and hence alerting the public.