# Research on Coal and Gas Outburst Risk Warning Based on Multiple Algorithm Fusion
## 1 Introduction  
Coal and gas outbursts are a frequent and dangerous occurrence in the coal mining industry. These accidents pose a significant threat to both the equipment used in coal mining and the safety of miners. As mining operations continue to deepen and intensify, the frequency of coal and gas outburst accidents has been on the rise, greatly impacting the safety of coal mine production. In fact, China has experienced a significant number of these accidents, accounting for over 40% of the global total. As of 2022, approximately 33.6% [1] of Chinese coal mines are classified as high-risk mines prone to coal and gas outbursts. To address these challenges and support the goals outlined in the national \"14 th Five-Year Plan\", experts and scholars have been conducting research and analysis to understand the mechanisms and risks involved in coal and gas outbursts. Their aim is to predict and prevent such accidents in order to ensure the safe production of coal mines and contribute to the sustainable development of China's energy sector. Coal and gas outburst accidents are the result of complex phenomena, driven by the dynamic interaction of coal and gas within the mine. This involves the uncontrolledevolution of various nonlinear factors, making it a highly destructive gas dynamic phenomenon [2]. In studying the mechanisms behind these outbursts, scholars have examined multiple factors such as ground stress, gas properties, and coal mechanical properties. They have constructed theoretical models based on traditional algorithms, although traditional algorithms often struggle to analyze the nonlinear relationship between these factors. With advancements in intelligent algorithms and machine learning, more and more experts and scholars are utilizing these technologies to analyze coal and gas outburst risks, yielding promising results. The challenge lies in uncovering the nonlinear relationships between various influencing factors, accurately predicting the risk and severity of coal seam gas outbursts, and implementing early warning systems to prevent or mitigate the disasters caused by these outbursts. This has become a critical task in ensuring the safe production of mines [3]. Over the past century, experts and scholars from both domestic and international institutions have conducted extensive research on the mechanism of coal and gas outbursts and have proposed numerous hypotheses. However, there is still no unified theory that can completely reveal the development mechanism of coal and gas outbursts. The theory proposed by [PERSON] [4] analyzed the mechanism of pore pressure on coal and conducted deformation tests of coal rock under cyclic loading and unloading of pore pressure. [PERSON] [5] derived a relationship model between coal porosity and mechanical strength and used porosity and gas pressure to classify and predict the level of outburst hazards. [PERSON] [6] through theoretical analysis, pointed out that structurally weak coal has a lower bearing capacity, and the fine coal particles formed after fragmentation have an extremely fast gas desorption rate. This can sustain the development of outbursts, making structurally weak coal seams prone to coal and gas outbursts. [PERSON] [7] suggested that coal and gas outbursts are influenced by factors such as gas pressure, gas composition, coal permeability, and adsorption desorption characteristics, and analyzed the relationship between these influencing factors and the control of outbursts. [PERSON] [8] proposed a coal and gas outburst support model, and analyzed the mechanism of non synchronous deformation induced coal and gas outburst in soft and hard layer. [PERSON] [9] systematically summarized the research progress on coal and gas outburst mechanisms in China from three aspects: theoretical hypothesis, physical simulation, and numerical simulation. [PERSON] [10] described in detail the entire process of occurrence and development of outbursts based on numerical simulations and theoretical analysis.