Abstract:
Most coal mine accidents are attributed to the miner’s unsafe behavior. Regulating the safety attitude and thus enhancing
miners’ safety behavior are significant for accident prevention. Capturing the interrelations between risks is important to
understand and promote coal mining safety thoroughly. Therefore, this paper proposes the intelligent accident predictive
framework (IAPF) for monitoring and analyzing the safety hazards and safety behavior of underground coal mines. The
most significant variables involved in occupational accidents and their association rules have been identified. These rules
are composed of numerous predictor variables that cause accidents, describing their characteristics and environment. The
accident model path analysis demonstrates that adverse effects, risk-taking behaviors predict and job dissatisfaction an
increased number of injuries in mines. The IAPF model gives an outcome as an indicative risk score linked with the
identified accident-prone situation, based upon which an appropriate mitigation plan can be established. The results show
the most typical instant causes and the percentage of accidents with a basis in every connotation rule. The experimental
results of IAPF show the highest prediction ratio of 97.5%, safety rate of 96.3%, security rate of 95.4%, and lowest
accident rate of 22.6%, energy consumption ratio of 28.6%, carbon management ratio of 25.3% and hazard risk ratio of
20.2% compared to other methods.
