A Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines

The environment of underground coal mines (UCMs) is vulnerable to many environmental problems and consequential endangerment. Among those problems, mine fire is a liable threat that causes the loss of lives of mine workers and other valuable infrastructure and resources in the mines. Therefore, continuous monitoring is very important for early detection of mine fire in UCMs. Internet of Things (IoT), nowadays, is widely used for continuous monitoring of the environment of UCMs. For the purpose of monitoring, deploy sensor nodes in the region of UCMs to sense the local information of the environment and transfer that information to the sink for further process. As the gathered information is indistinct, it is necessary to consider the information for taking precautions. Therefore, we propose a method to predict the occurrences of mine fire in UCMs by using logistic regression model. Then, we applied active learning (AL) and semi-supervised learning (SSL) methods on the dataset sequentially to train the model by dividing the dataset into training and testing datasets. The training dataset is used to train a model. On the other hand, the testing dataset is used to evaluate the trained model. The overall process is implemented at the sink instead of control stations. In case of any hazardous situation, the sink takes immediate and necessary actions based on the sensed data rather than the control station. The trained model is simulated using WEKA tool for the data of continuous monitoring on different hazard conditions. This model is more reliable and works effectively when compared to offline monitoring system in any kind of hazards situations in UCMs. The simulated results show the accuracy of the proposed method as 98% whereas the accuracy of linear regression and naive Bayes has been shown as 93% and 96%, respectively, that shows our proposed method outperforms over the existing techniques.