Prediction of gas concentration in the upper corner of mining working face based on the FEDformer-LGBM-AT architecture

In the context of the intelligent upgrading of coal mines, mining high-quality information from massive monitoring data of working faces to construct scientific models that enhance prediction duration and accuracy is crucial for preventing excessive gas concentration in the upper corner. However, there are many factors that affect the gas concentration in the upper corner, and there is a lack of utilization of massive data. The prediction accuracy of gas concentration is high but the duration is short, only 0～30 minutes, while the prediction accuracy and generalization ability are poor for medium to long duration 30～60 minutes. In order to solve this problem, this article takes a coal mining face in Shanxi Province as the research object. Firstly, the coal seam gas content of the face is dynamically extracted, and a feature set of coal seam gas content, gas concentration, coal mining machine, and wind speed is constructed. Then, the feature set is preprocessed, and different features are screened based on correlation analysis. Further construct short-term trends, stable trends, periodic trends, and concatenated features of relevant features. Firstly, a gas concentration prediction layer based on frequency enhanced decomposition transformer (FEDformer) is constructed, and a residual correction layer based on lightweight gradient boosting machine (LGBM) is constructed. Then, adaptive thresholding (AT) technology is introduced to construct a threshold perception layer. Finally, a three-layer gas concentration prediction model architecture is formed to predict the gas concentration in the upper corner within the next 60 minutes, and the prediction performance was investigated by recalling rate, false positive rate, MAE and MAPE. The research results indicate that the short-term recall rate of the upper corner gas concentration prediction model based on the FEDformer-LGBM-AT architecture is 0.956, the false alarm rate is 0.035, the MAE is 0.033, and the MAPE is 0.183; The recall rate of long-term prediction is 0.940, the false positive rate is 0.035, the MAE is 0.047, and the MAPE is 0.262; Compared with traditional models such as Grey Model (GM), Support Vector Machine (SVM), Backpropagation (BP), Gated Recurrent Unit (GRU), Particle Swarm Optimized Long Short Term Memory (PSO-LSTM), Transformer, etc., the FEDformer-LGBM-AT architecture model has better long-term prediction accuracy and generalization ability. The adaptive threshold perception makes the model sensitive to high-value gas concentrations. This architecture model compensates for the limitations and generalization of short-term prediction, supports on-site gas exceedance prevention measures, and can provide certain reference and guidance for intelligent prediction of gas concentration in mining face.