# Research on Coal and Gas Outburst Risk Warning Based on Multiple Algorithm Fusion
## 3 An Account of Stacking-Framed Coal and Gas Outburst Risk Warning Model  
All data undergo pre-training data cleaning, data filtering, and data sorting. The influence of dimensionality differences among different indicators is removed through standardization processing. Missing values are filled in using data cleansing techniques, and certain data points are identified or removed. Once data processing is complete, model training can commence.  
Step 1: Training feature model. In the known presence ofIn the data sample of features, each dataset has its own characteristics. One feature of the given data sample is used as a label, and the rest of the feature are used as elements of the new feature vector to form a new training sample.  
Step 2: Sampling features to generate virtual data. From the collection, replace a value of the sample: the The value is the characteristic of the temporarily synthesized data sample. 1. The first eigenvalue, and then the remaining features are taken in turn, and finally you can getthe eigenvalues of the feature.  
Step 3: The features to be generated in the temporary data sample 1. As an output, characteristics 2 and other features are used as input respectively... Put in the feature matrix and put The output value of the model is characteristic.1 The virtual generation value of In the same sense, all virtual values are obtained.  
Step 4: Based on the original data and the newly generated data, calculate the grey correlation coefficient between the degree of gas outburst and various factors.  
\[y(x_{0}(k),x_{i}(k))=\frac{a+\rho b}{|x_{0}(k),x_{i}(k)|+\rho b} \tag{21}\]  
Step 5: Calculate the grey correlation between each subsequence, and sort each indicator according to the grey correlation.  
\[r_{i}=\frac{1}{n}y(x_{0}(k),x_{i}(k)) \tag{22}\]  
Step 6: Divide indicator groups based on the size of grey correlation degree, and use XGBoost, RF, GBDT, and SVM training data to comprehensively compare MAE, MSE, and R2 in each indicator. Select the data group with the smallest error and the best fit degree as the main control factor.  
Step 7: Based on the main control factors of coal and gas outburst, SVM, XGBoost, GBDT, and RF training models are selected to predict the situation of gas outburst.  
Step 8: Using the above predictions on the training set as the training set and the predictions on the test set as the test set, retrain the data to obtain new prediction results. Stacking combines steps 7 and 8 together, using the output of step 7 as the input to step 8 to obtain the final output result.  
Step 9: Compare the training effects of SVM, XGBoost, RF, GBDT, and Stacking fusion models under the main control factors, and select the Stacking fusion model with the best prediction effect to construct a gas outburst risk warning model.  
The occurrence of coal and gas outbursts is comparatively intricate. It is influenced by multiple factors and exhibits a certain degree of nonlinearity. This article employs grey relational analysis to explore the nonlinear relationship between coal and gas outburst issues. It aims to unveil the correlation between influencing factors and the occurrence of outbursts. A set of key controlling factors that impact gas outburst situations is selected. By doing so, the prediction work is streamlined, resulting in improved model forecasting performance.  
Due to variations in gas outburst factors among different mines, there is a certain relationship between these factors and the mining environment and coal occurrence conditions. Therefore, when constructing a gas outburst risk warning model, the model's generalization ability should be considered. Integrated models have better generalization ability compared to single machine learning models, which can improve the accuracy of the model. Classic methods of integration include bagging, boosting, and stacking. The main idea of stacking ensemble learning is to combine multiple models and fully utilize their respective advantages to make final predictions and achieve the best results. As shown in Figure 3.