# Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM
## 1 Introduction  
According to the survey and statistics [1; 2], coal mine accidents in 2021 will cause more losses than in 2020. According to the accident data of coal mines in China from 2008 to 2021, gas outburst is one of the main types of coal mine accidents, accounting for 64.3% of the total number of coal mine accidents. Such accidents will cause casualties, damage to equipment and facilities, disrupt the production system, and cause other significant accidents [3]. In the process of coal mine production, if the hidden danger cannot be found and eliminated in time, it will hinder safe production and cause economic losses. Therefore, it is of great significance to study and predict the risk of gas outbursts to reduce the incidence of accidents, and to ensure the safety of coal mine production. As coal mine gas outburst is a highly complex dynamic phenomenon, a reasonable prediction index should be established when predicting the risk of gas outburst [4]. In many studies on predicting gas outburst risk, most scholars use the characteristics of coal and gas and geological structure to establish a prediction index system [5]. However, the occurrence of gas outburst accidents is not only related to these unsafe conditions, but also closely related to unsafe behavior in the coal mine production process. This paper analyzes the gas outburst accident report from the gas outburst prevention and control process perspective, when establishing the risk prediction index system. Analyzing the accident report establishes a comprehensive gas outburst prediction index system. There are two methods to predict the risk of gas outburst: using numerical simulation to predict [6; 7; 8]; and combining artificial intelligence with prediction [3]. Prediction employing artificial intelligence is the current mainstream research direction [9]. For example, the BP algorithm [10]; the ELM and Kernel Principal Components Analysis (KPCA) combined algorithm [11]; the Association Rule [12]; ELM [13]; Support Vector Machine (SVM) [14]; the Long Short-Term Memory (LSTM) network algorithm [15]; and other algorithms, are used to predict gas outburst. Among them, the BP neural network has a solid fitting ability, and this algorithm is suitable for simple prediction, although not complex model prediction [16]. Compared with BP, the prediction accuracy of LSTM is relatively good. [PERSON] and other scholars [17] used the ISSA algorithm to optimize LSTM and found the best parameters and working conditions of the LSTM algorithm. After the experiment, they found that the LSTM model still had limitations in improving the prediction effect. The SVM is a more traditional prediction model, which shows good advantages in linear prediction, but has a poor effect in solving nonlinear prediction [18]. The KPCA algorithm reprogrammes the problem by reducing dimensions, which has certain limitations in dealing with nonlinear prediction [19]. The learning algorithm is developing continuously, but in the development process it is found that the gradient-decreasing learning algorithm has shortcomings, such as overfitting, slow iteration speed, and the low accuracy of the final result. [PERSON] [20] and other scholars developed the ELM to avoid the hindrance of these shortcomings to research. This algorithm belongs to the single-layer neural network. The emergence of the ELM integrates the advantages and improves the disadvantages of general neural networks. The ELM has certain randomness in selecting initial weights and thresholds during training, leading to poor model stability and poor results accuracy [21]. Given the above problems, many scholars have used Genetic Algorithms (GA) or Particle Swarm Optimization (PSO) with search ability to optimize the ELM. For example, [PERSON] et al. [22] used PSO to optimize the ELM, thereby establishing a safety evaluation model for coal and gas outburst; [PERSON] et al. [23] used the GA algorithm to optimize the ELM and identified the water source after establishing the model, to improve accuracy.