The study of coal mines in China compared 10 machine learning algorithms to see which AI method could make predictions about changes in methane gas levels 30 minutes in advance, and notify users of anomalies.
Gas explosions or ignitions in underground mines pose significant risks, with almost 60 per cent of coal mine accidents in China caused by methane gas.
China accounted for 46 per cent of the world’s coal production in 2020, and more than 3200 coal mines in the country with high gas content at outburst-prone risk levels.
Author and Charles Darwin University (CDU) Faculty of Science and Technology Adjunct Associate Professor Niusha Shafiabady said the results showed out of the 10, four machine learning algorithms produced the best results.
“Linear Regression is one of the most efficient algorithms with better performance for short-term forecasting than others,” Associate Professor Shafiabady said.
“Random Forest frequently shows a statistically lower error performance and achieves the highest prediction accuracy. Support Vector Machine performs well and has a shorter computational time on small datasets but will require too much training time as the dataset size increases.
“The findings of this study will help the coal mining industry to reduce the risk of accidents such as gas explosions, safeguard workers, and enhance the ability to prevent and mitigate disasters which will lead to financial losses in addition to potential losses of lives.”
The study was conducted with Charles Darwin University, the University of Technology Sydney, Australian Catholic University, Shanxi Normal University, and Central Queensland University.
Associate Professor Niusha Shafiabady, who is also a researcher at Australian Catholic University’s Peter Faber Business School, said there were multiple applications for these results.
“This method works for all coal mines, and the same principles can apply to other industries such as aerospace, oil and gas, agriculture and more,” she said.
“This is an example of an application where AI can be used to save lives and mitigate health and safety risks.”
