Coal mine gas concentration prediction technology based on machine learning
Before a gas disaster occurs in a coal mine,it is often accompanied by abnormal gas concentration.Accurately de-termining gas concentration is the basis for gas outburst prediction,ventilation design,and other related work.We use ma-chine learning method to comprehensively calculate various factors that affect gas concentration,and explore a multi-factor coal mine gas concentration prediction technology based on machine learning.By constructing a model,training the model,and using it in practice,the error between the predicted and the actual value is calculated,and the feasibility of this method verified through actual production mine data.The gas prediction technology is transformed from passive to active,providing a new approach for predicting coal mine gas concentration.Introducing intelligent technologies such as big data and deep learning into coal mine gas management is of widely practical prospect.
万方数据
维普
