A Multi-Criteria Decision Intelligence Framework to Predict Fire Danger Ratings in Underground Engineering Structures

Abstract

A wide variety of natural catastrophes are induced by coal mining, with fire hazard being one of the most significant threats to underground engineering structures. In recent years, there has been an alarming rise in mine fire accidents due to the abundance of coal deposits around the world. Underground fires and explosions have continuously been the primary reason for a significant proportion of deaths and the destruction of infrastructure over the last few decades. Underground mining fires deplete natural coal resources, have an adverse impact on the environment by releasing hazardous chemicals and greenhouse gases into the atmosphere, and cause subsidence due to coal depletion during the combustion process. This study aims to predict fire danger rating of underground mining production processes by using the application of state-of-the-art unsupervised and supervised machine learning techniques. The developed k-nearest-neighbors-based isometric feature mapping and fuzzy c-means clustering algorithm has shown its dependability and superiority with a higher accuracy and has been advantageous to the monitoring and prevention of fire danger in underground mining production processes. The proposed multi-criteria decision intelligence framework permits early fire detection, providing the emergency response team extra time to respond the critical situations in order to prevent the fire from spreading, hence promoting sustainable, green, climate-smart, environmentally friendly and safe mining engineering operations.

Decision Intelligence-Based Predictive Modelling of Hard Rock Pillar Stability Using K-Nearest Neighbour Coupled with Grey Wolf Optimization Algorithm

“…It is used in the task of undertaking scientific and computational studies on enormous databases. Python is a language for programmers that is capable of being employed for assisting with procedures and algorithms that focus on the sorting and organising stages of the data mining process [74]. When it comes to creating flexible By incorporating decision intelligence approaches into mining engineering practices, mining businesses may make data-driven decisions, optimise operations, increase safety, reduce environmental impact, and boost overall productivity and profitability.…”

“…It is used in the task of undertaking scientific and computational studies on enormous databases. Python is a language for programmers that is capable of being employed for assisting with procedures and algorithms that focus on the sorting and organising stages of the data mining process [74]. When it comes to creating flexible applications, this option is among the best options to choose because it provides such a wide variety of valuable perks.…”

Proposed Changes to the Inspection Strategy for Fire Alarm Systems: Empirical Analysis of Weak Points and Technical Influencing Factors

“…As part of the discussion of the results, it will be considered why further development of the inspection strategies for fire alarm systems is necessary. The results are presented using the PESTEL methodology [10]. The PESTEL method is a strategic tool that helps to analyze the external environment and identify the macroeconomic factors that influence the area under investigation.…”

“…The further development of maintenance and servicing approaches is only considered in a very small part of the research. One example of this is given by the study by Kamran et al in [10]. Here, as in many other research projects, the authors analyzed how the use of machine learning can optimize fire detection in mine fires.…”

“…There is a large body of research on the technical advancement of fire detection technology in the field of fire alarm systems and their related components. The studies especially focus on the sensor technology employed, the fire parameters investigated, and the algorithms for assessing the systems [10,[13][14][15][16][17][18]. Furthermore, research has been conducted on the systems' power supply and availability [19,20].…”