Hydraulic support pressure prediction via deep learning with multilevel temporal feature integration

Accurate prediction of hydraulic support pressure is crucial for ensuring coal mine safety. With increasing mining depths and increasingly complex operating environments, precise prediction faces greater challenges. To address these challenges, this study proposes an LSTM-PatchTST prediction method based on multi-dimensional feature dependency fusion: First, Pearson correlation analysis is used to screen key features, and Gaussian moving average filtering optimizes the data; Subsequently, the preprocessed time series is input to the LSTM network, where the forget gate and input gate capture short-term fluctuations and long-term trends respectively, while residual connections ensure complete preservation of multi-layer temporal features; Then, the dynamic features extracted by LSTM are passed to the PatchTST module, which divides the sequence into local patches, with a multi-layer self-attention encoder simultaneously modeling local details and global dependencies, achieving deep feature fusion. The model's performance is validated using actual pressure data from Fucun Coal Mine in Zaozhuang, Shandong. Experimental results show that, compared to pure PatchTST and Transformer + LSTM models, the proposed model reduces RMSE by approximately 48.6% and 30.0%, and MAE by approximately 58.7% and 38.8%, respectively. Finally, to further verify the model's generalization ability, the trained model was transferred to a dataset from Gengcun Coal Mine in Yima, Henan, where compared to pure PatchTST and Transformer + LSTM models, the proposed model reduces RMSE by approximately 34.6% and 31.4%, and MAE by approximately 35.7% and 29.9%, respectively.