Abstract

For our country, coal might be a very important common resource. There are many dangerous circumstances, such as temperature and humidity increases and the release of harmful gases. These circumstances put specialists' lives at danger and create an unsafe workplace for them to perform in. As a result, maintaining the safety of the mine's workers has become a serious concern. It is quite difficult for human beings to continuously monitor all environmental variables in a coal mine. A duty-bound setup can be used in conjunction with the approach to feel the mine's original atmosphere and assess whether it is appropriate for a person's presence. If this is not the case, the system should warn the worker of the danger. The structure should be able to avoid the destruction in this manner. Because of this, methods for combining IoT and machine learning forecasting, such as ensemble learning, are prepared to continuously take test data from the coal mine environment and use past conditions to predict threats.

Description