Early Warning System for Seismic Events in Coal Mines Using Machine Learning

N 2015, the mining industry in Poland reported 2158 dangerous incidents with 19 casualties and 12 severe injuries [1]. Underground mining work poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. Monitoring and decision support systems might play an essential role in limiting the number of incidents and their prevention. Such systems, often based on machine learning or data mining techniques, can be effectively applied to lessen the danger to employees and prevent potential losses arising from lost and damaged equipment, see, e.g., [2], [3], [4]. In this paper, we present a model for predicting dangerous seismic events in coal mines.