Prediction of Gas Emission by BP Neural Network Based On Wavelet Analysis

Guo, Xiucai; Ren, Zhi-Qi; Wang, Qinsheng; Cui, Chenchen

doi:10.1088/1755-1315/252/5/052046

Cited by 3 publications

(2 citation statements)

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“…The back propagation neural network (BPNN) is a multi-layer network that forwards signals and propagates errors backwards. BP neural network has been studied and applied to solve many problems ( 68 – 72 ), and is one of the most widely used networks at present. It can simulate the information transmission mode of human brain neurons, perform non-linear transformation and regression processing on complex information variables, and obtain operation results with a high fitting degree.…”

Section: Methodsmentioning

confidence: 99%

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253).

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Section: Methodsmentioning

confidence: 99%

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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“…In recent years, many scholars have proposed many gas emission prediction models, such as the SVR model [6], PCA-ELM model [7], BP neural network model [8], and SAPSO-ELM model [9], PCA-GA-BP model [10], etc. Although using the above prediction models in the gas emission prediction improves the accuracy, the ELM algorithm, and BP algorithm are not suitable for small sample prediction problems such as gas emission, as they are easy to cause inade-quate learning phenomenon, the accuracy, and stability of gas emission prediction models need to be further improved.…”

Section: Introductionmentioning

confidence: 99%

Prediction of coal mine gas emission based on hybrid machine learning model

Shao

et al. 2022

Earth Sci Inform

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Coal mine gas accident is one of the most significant threats to the safe mining process in coal mines, so it is very important to accurately predict coal mine gas emission. To improve the accuracy of coal mine gas emission prediction, a hybrid machine learning prediction model combining random forest (RF) algorithm, improved gray wolf optimizer (IGWO) algorithm and support vector regression (SVR) algorithm is proposed. Thirty groups of actual measured gas emission data from a coal mine are selected as samples, and the latter five groups are used as test sets.Firstly, the RF algorithm is used to screen 13 influencing factors of coal mine gas emission, and finally six influencing factors are selected as the input variables of the prediction model; Secondly, the IGWO algorithm is obtained by improving the GWO algorithm with a nonlinear convergence factor and a DLH search strategy; Finally, the IGWO algorithm is used to optimize the SVR algorithm to establish the RF-IGWO-SVR model, and the model is compared with other models to verify its superiority. The results show that the average relative error of the RF-GWO-SVR model is 1.55%, and this result is better than other comparative models, which indicates that the model can effectively improve the prediction accuracy of coal mine gas emission and provide a new model for coal mine gas emission prediction.

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Prediction of coal mine gas emission based on hybrid machine learning model

Shao

et al. 2022

Preprint

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Coal mine gas accident is one of the most significant threats to the safe mining process in coal mines, so it is very important to accurately predict coal mine gas emission. To improve the accuracy of coal mine gas emission prediction, a hybrid machine learning prediction model combining random forest (RF) algorithm, improved gray wolf optimizer (IGWO) algorithm and support vector regression (SVR) algorithm is proposed. Thirty groups of actual measured gas emission data from a coal mine are selected as samples, and the latter five groups are used as test sets. Firstly, the RF algorithm is used to screen 13 influencing factors of coal mine gas emission, and finally six influencing factors are selected as the input variables of the prediction model; Secondly, the IGWO algorithm is obtained by improving the GWO algorithm with a nonlinear convergence factor and a DLH search strategy; Finally, the IGWO algorithm is used to optimize the SVR algorithm to establish the RF-IGWO-SVR model, and the model is compared with other models to verify its superiority. The results show that the average relative error of the RF-GWO-SVR model is 1.55%, and this result is better than other comparative models, which indicates that the model can effectively improve the prediction accuracy of coal mine gas emission and provide a new model for coal mine gas emission prediction.

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Prediction of Gas Emission by BP Neural Network Based On Wavelet Analysis

Cited by 3 publications

References 0 publications

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Prediction of coal mine gas emission based on hybrid machine learning model

Prediction of coal mine gas emission based on hybrid machine learning model

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