Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

Yang, Li; Fang, Xin; Wang, Xue; Li, Shanshan; Zhu, Junqi

doi:10.3390/ijerph191912382

Cited by 5 publications

(4 citation statements)

References 46 publications

(49 reference statements)

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“…ELM can achieve its learning objectives by increasing the number of hidden layers [30]. Table 4 depicts the association between the training set's coefficient of determination and the quantity of the IHPO-ELM hidden layer nodes.…”

Section: The Training Ihpo-elm Model's Network Performancementioning

confidence: 99%

Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm

Wang

Ji²,

Shao³

et al. 2023

Sustainability

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Considering the volatility and randomness of wind speed, this research suggests an improved hunter-prey optimization (IHPO) algorithm-based extreme learning machine (ELM) short-term wind power prediction model to increase short-term wind power prediction accuracy. The original wind power history data from the wind farm are used in the model to achieve feature extraction and data dimensionality reduction, using the partial least squares’ variable importance of projection (PLS-VIP) and normalized mutual information (NMI) methods. Adaptive inertia weights are added to the HPO algorithm’s optimization search process to speed up the algorithm’s convergence. At the same time, the initialized population is modified, to improve the algorithm’s ability to perform global searches. To accomplish accurate wind power prediction, the enhanced algorithm’s optimal parameters optimize the extreme learning machine’s weights and threshold. The findings demonstrate that the method accurately predicts wind output and can be confirmed using measured data from a wind turbine in Inner Mongolia, China.

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Section: The Training Ihpo-elm Model's Network Performancementioning

confidence: 99%

Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm

Wang

Ji²,

Shao³

et al. 2023

Sustainability

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“…Yang, L. et al [10] proposes a deep coal-gas outburst risk prediction method based on kernel principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm, the accuracy rate of which was as high as 100%. Zhang Ruili et al [11] proposes the use of a coupled fault tree analysis (FTA) and artificial neural network (ANN) model to improve the prediction of the potential risk of coal and gas outburst events during the underground mining of thick and deep Chinese coal seams.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Convolutional computation is performed at each layer, and the convolutional result is output after multiple rounds of convolution. The feeling field computation formula is shown in Equation (10).…”

Section: Tcnmentioning

confidence: 99%

ISCSO-PTCN-BIGRU Prediction Model for Fracture Risk Grade of Gas-Containing Coal Fracture

Fu,

Lei

2023

Processes

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A multi-strategy improved sand cat swarm algorithm with PTCN-BIGRU is proposed to solve the problem of predicting the risk level of gas-containing coal fracture. Combined with kernel entropy component analysis to downscale the gas-containing coal fracture risk level predictors, TCN is used for feature extraction by parallel convolution operation, and BiGRU is used to further obtain the contextual links of the features. A parameterized exponential linear unit based on the standard TCN is used to improve the linear unit and to enhance the generalization capability of the model. Combined with the sand cat swarm optimization algorithm to determine the optimal BIGRU network parameters, Singer chaos mapping, chaos decreasing factor, and adaptive t-distribution are used to improve the SCSO for optimal risk level prediction accuracy. The results show that the prediction accuracy of the ISCSO-PTCN-BiGRU model is 93.33%, which is better than other models, and it is proved that this paper can effectively improve the prediction accuracy of gas-containing coal fracture risk level. This research adds a theoretical support for the prevention of gas protrusion accidents and a guarantee for the safety of underground production in coal mines.

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“…On the other hand, model-free methods, alternatively, do not formulate any specific model for motion characteristics, as an alternative to forecast the location by taking into account the preceding observations. Existing model-free approaches include support vector regression (SVR) [10,11], relevance vector machines (RVM) [12,13], extreme learning machine (ELM) [14][15][16][17][18], random vector functional link (RVFL) [19], convolution neural network (CNN) [20], long short-term memory (LSTM) [21][22][23], and forecasting random convolution node (fRCN) [24]. A recent comprehensive evaluation of existing approaches [7] reveals that model-free approaches predict respiratory motion with greater robustness and less prediction error than model-based counterparts, as a result, yielding improved prediction performance, especially at larger prediction lengths.…”

Section: Introductionmentioning

confidence: 99%

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

Rasheed,

Veluvolu

2024

Mathematics

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The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

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Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

Cited by 5 publications

References 46 publications

Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm

Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm

ISCSO-PTCN-BIGRU Prediction Model for Fracture Risk Grade of Gas-Containing Coal Fracture

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

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