Sequential pattern mining and matching method with its application on earthquakes

Abstract

As one of the important methods on prediction, data mining plays a significant role specifically in the field of abnormal prediction to ensure security. Based on the remote sensing data of the sun-synchronous polar orbit EOS-AQUA satellite of USA, this paper proposes an abnormal pattern detection method with sequential pattern mining and matching. First of all, based on the selected observation area, abnormal sequential patterns are mined and frequent abnormal sequential patterns are formed. Then, seismic sequential pattern is generated, and the matching algorithm of earthquake is established. Finally, the accuracy rate and the false positive rate of prediction are worked out. All experiments are conducted with the remote sensing satellite from 2005 to 2014, and the experimental results are interesting. According to the carbon monoxide content, the accuracy rate is 65% while the false positive rate is 15% by using the data of 30 days before earthquake for prediction.