IM-NKA: A Natural Killer cell Algorithm for earthquake prediction based on extremely imbalanced precursor data

Abstract

Earthquake prediction plays a vital role in reducing the risk of earthquakes to human beings. However, the precursor seismic data is extremely imbalanced, resulting in unsatisfactory performances of standard algorithms on earthquake prediction. In this paper, we propose a Natural Killer cell Algorithm inspired by the Induction and phenotype discrimination Mechanism of natural killer cells (IM-NKA) for earthquake prediction. IM-NKA first generates induction pathogens (synthetic samples) in the hyper-sphere area around each pathogen (selected minority instance), called NKI. After balancing the dataset, the IM-NKA generates the phenotype detectors. Then, the IM-NKA is used to predict earthquakes with a magnitude larger than 3.5 in the next day. The experiment consists of two parts: the first part compares the NKI with four oversampling methods and demonstrates that using NKI as an oversampling method can effectively improve the quality of synthetic samples for unbalanced earthquake prediction; the second part compares the performance of IM-NKA with eight different classifiers. The experiment results show that the performance of earthquake prediction using IM-NKA also significantly outperforms other classifiers.