Prediction of Hidden Dangers in Mine Production Using Timeliness Managing Extreme Learning Machine for Cloud Services

Abstract

Recently, there has been an ever-increasing interest in the study of data-driven analytics to predict hidden dangers in the cloud service-based coal mine production, with the purpose of the prevention of possible accidents. In this paper, to achieve the above prediction, a machine learning algorithm based on the single-hidden layer feed forward network (SLFN) using timeliness managing extreme learning machine (TMELM) is utilized. Compared with those traditional learning algorithms, extreme learning machine (ELM) has its unique feature of a higher generalization capability at a much faster learning speed. In addition, the timeliness managing ELM has been proposed by incorporating timeliness management scheme into ELM approach. Under the timeliness managing ELM scheme used to predict the hidden dangers, the newly incremental data could be prior to the historical data while maximizing the contribution of the newly increasing training data, since it may be more feasible that the incremental data can contribute reasonable weights to represent the current production situation in accordance with the practical analysis for accidents in coal mine production. The experimental results on the coal mines of Beijing show that by using timeliness managing ELM, the prediction accuracy of hidden danger can be improved with better stability compared with other similar machine learning methods.