# Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM
## 1 Introduction
It can be seen that the GA algorithm and the particle swarm optimization algorithm are helpful for gas outburst prediction. However, there are defects in using GA and PSO optimization; the convergence speed is slow, and it is easy to fall into local optimization [24]. The WOA algorithm requires few parameters, has good stability and optimization ability, and is not easy to fall into local optimization [25]. The optimal initial weight and offset value of the ELM algorithm can be found through optimization, so we propose the WOA-ELM prediction model. The accuracy of prediction results is the primary goal that many scholars achieve through machine learning. However, through the prediction results, only the current gas outburst risk value or risk level can be determined, and how to do it under this risk is a problem that should be considered in reality. As a kind of artificial intelligence technology, CBR has been deeply studied by many scholars in coal mine early warning [26; 27; 28]. In the application process, CBR will also be combined with prediction. For example, [PERSON] et al. [29; 30] combined Principal Component Analysis (PCA) with the CBR method to predict the risk of coal and gas outburst accidents, improving the accuracy and stability of prediction results. This scholar also proposed gas outburst risk prediction using CBR and data fusion technology. [PERSON] et al. [31] took the gas outburst accident as an example to analyze the rationality of the CBR application in gas outburst accident early warning. In the above application process, CBR's role is to improve prediction accuracy. However, the main idea of CBR is to judge the current case by comparing the similarity between the current case and the historical case information. Scholars such as [PERSON] [32] applied the core step in CBR (case retrieval) to study coal mine gas explosions to ensure that the retrieval of available information is helpful to the current risk. Therefore, CBR can be used to manage the prediction results further, and corresponding measures and suggestions can be put forward according to the different levels of gas outburst, to reduce the risk of gas outburst. To sum up, the WOA can improve the iteration speed and is not easy to fall into the local optimum. In this paper, we use the WOA algorithm to optimize the ELM, find the optimal input weight and offset value of the ELM through WOA, and improve the accuracy of the ELM prediction and the algorithm's stability. In addition, more scholars focus on the accuracy of the prediction results without further managing the prediction results. For managers, although the prediction results are essential, the prediction results cannot give good suggestions. Therefore, CBR works on the prediction results and proposes corresponding measures and recommendations according to the related risks. The suggestions are of a high reference value. The rest of this paper is structured as follows: The second part introduces the theoretical methods and model building used in this paper; the third part introduces the index system of gas outburst risk prediction; the fourth part introduces the source, quantification, and normalization of data; the fifth part introduces the results and discussion after the data is substituted into the model; and the sixth part is the conclusion.