Introducing macrophages to artificial immune systems for earthquake prediction

Abstract

Earthquake prediction (EQP) is crucial for taking preemptive measures and accurately predicting damage. Several historical seismic-event-based EQP approaches have been proposed; however, these approaches only identify anomalies without distinguishing noise, reducing the prediction accuracy. Macrophages play an important role in the immune system by recognizing viruses, apoptotic cells, and normal cells, as well as performing immune responses and suppression to ensure homeostasis; that is, macrophages exhibit strong classification capabilities and self-adaptability. Therefore, in this study, a novel artificial macrophage algorithm (AMA) for EQP is proposed. More specifically, we first introduce the biological mechanism of macrophages to establish recognition and learning mechanisms to identify noise and anomalies. Second, we adopt a distance metric to denote the weights of the AMA, instead of using experience-based parameters. Finally, a stochastic gradient descent is introduced to ensure the adaptability of the AMA. The performance of the AMA was assessed through an analysis of historical seismic events in Sichuan and its surroundings. Our experimental results demonstrate that AMA outperforms state-of-the-art EQP algorithms. The parameters and statistical tests of AMA were further analyzed in this study.