Research on Gas Outburst Prediction Model Based on Multiple Strategy Fusion Improved Snake Optimization Algorithm With Temporal Convolutional Network

Abstract

A gas outburst prediction model based on multiple strategy fusion and improved snake optimization algorithm (MFISO) and temporal convolutional network (TCN) is proposed to address the problems of low accuracy of deep learning prediction models for gas outburst in underground mines. By adopting the phase space reconstruction method, the time series of multiple complex influencing factors related to gas outburst were reconstructed and used as model inputs. Sine chaos mapping, spiral search strategy and snake dynamic adaptive weight are introduced to improve the snake optimization algorithm (SO), which enhances the local optimal escape capability and global search capability of the algorithm. The tangent-based rectified linear unit (ThLU) was used to improve the rectified linear unit (ReLU) of the standard TCN to strengthen the generalization capability of the model. The MFISO algorithm was used to optimize the relevant hyperparameters of the improved TCN model to optimize the accuracy of gas outburst prediction. The TCN, GRU, LSTM, SO-TCN, WOA-TCN, and PSO-TCN prediction models were selected to compare the prediction performance with the MFISO-TCN gas outburst prediction model, and the results showed that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MFISO-TCN model were 3.11%, 0.47% and 3.31% are lower than those of other models, which verifies that the method of this paper effectively intensifies the performance of gas outburst prediction model in underground mines.INDEX TERMS Gas outburst, phase space reconstruction method, multiple strategy fusion, snake optimization algorithm, tangent-based rectified linear unit, temporal convolutional network.

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

“…The SO algorithm is a new intelligent optimization algorithm proposed by Hashim et al that turns the feeding, fighting, and mating behavior of snakes into a mathematical mode [ 31 , 32 , 33 , 34 ]. Feeding is divided into two phases: the exploration phase and the exploitation phase.…”

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

“…As SO is prone to falling into local optimality and inadequate optimization capabilities when faced with different optimization problems, enhanced versions of it have been proposed successively to achieve better results. A multi-strategy fused snake optimizer (MFISO) was developed by Fu et al for deep-learning prediction models of gas prominence in underground mines [ 53 ]. Rawa investigated a hybrid form of SO and sine cosine algorithm (SCA) called SO-SCA using both parallel and tandem mechanism runs and used it to solve transmission expansion planning models [ 54 ].…”

Kaoti̇k Yilan Opti̇mi̇ze Edi̇ci̇

“…(Liu et al 2023) proposed a chaotic gaussian snake optimization algorithm for sensor node optimization in soil monitoring wireless sensor networks. (Fu et al 2022) proposed a gas explosion prediction model in which the improved snake optimization algorithm is integrated. Sine chaos mapping, spiral search strategy, and snake dynamic adaptive weight were used in the snake optimizer to increase the search capability.…”