基于优化马尔可夫模型的煤矿事故死亡人数预测

Prediction of death toll in coal mine accidents based on optimized Markov model

为准确预测煤矿事故死亡人数,分析灰色模型、维新灰色模型和无偏灰色模型3种灰色模型在煤矿事故死亡人数预测方面的适用性和应用性。首先,采用后残差比、平均相对误差和小误差概率3个指标,分别分析3种模型的预测准确性;然后,用马尔可夫模型优化修正最佳的灰色模型预测结果;最后,预测2020—2022年煤矿事故死亡人数。结果表明:原始数据的区间选择对预测精度影响较大,应尽量选择预测精度较好的数据区间段;通过对比分析3种模型的预测精度,无偏灰色模型为最佳模型;无偏灰色马尔可夫模型不但能消除灰色模型的固有偏差,而且能提高预测精度。该模型平均预测精度达到93.8%。

In order to accurately predict death toll in coal mine accidents,applicability of three prediction models was analyzed,including grey model,renewal grey model and unbiased grey model.Firstly,their prediction accuracy was analyzed by adopting posterior residual ratio,mean relative error and small error probability.Then,optimized grey model prediction results was revised with Markov model.Finally,death number of coal mine accidents from 2020 to 2022 was predicted.The results show that the interval selection of original data has a great influence on prediction accuracy,so these with better accuracy should be selected as much as possible.The unbiased grey model proves to be the best one by comparing and analyzing prediction accuracy of all three models.The unbiased grey Markov model can not only eliminate inherent deviation of grey model,but also improve prediction accuracy with an average accuracy of 93.8%.