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- W4386868319 abstract "In order to improve the prediction accuracy of underground gas gush quantity, a gas gush quantity prediction model based on improved Archimedes algorithm (IAOA) optimized long short-term memory network (LSTM) is proposed. Firstly, we adopt the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to reconstruct the absolute gas gush volume time series, and apply the reconstructed gas gush volume components of different frequencies to the LSTM. Neural network for high-frequency components, low-frequency components and trend terms are predicted, and finally the prediction results are superimposed and summed to obtain the final predicted value of gas outflow. Finally, the model is validated with actual sample data and compared with LSTM, GRU and AOA-LSTM models, and the model proposed in this paper has better prediction effect. The average relative error and root mean square error were reduced to 0.563% and 1.026%, respectively. the results showed that the proposed model based on IOA-LSTM gas gush prediction effectively improved the accuracy of underground gas gush prediction." @default.
- W4386868319 created "2023-09-20" @default.
- W4386868319 creator A5019236522 @default.
- W4386868319 creator A5034634803 @default.
- W4386868319 date "2023-04-21" @default.
- W4386868319 modified "2023-09-27" @default.
- W4386868319 title "Research on gas gush prediction model based on multi-strategy improved Archimedes algorithm to optimize long and short time memory network" @default.
- W4386868319 doi "https://doi.org/10.1109/icsp58490.2023.10248448" @default.
- W4386868319 hasPublicationYear "2023" @default.
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