Research on Rockburst Risk Level Prediction Method Based on LightGBM−TCN−RF

Ma, Li; Cai, Jiajun; Dai, Xinguan; Jia, Ronghao

doi:10.3390/app12168226

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…The temporal convolutional network is improved based on the traditional 1D convolutional neural network while combining causal convolution, dilated convolution and residual linking. Some researchers have applied TCN model to predict short-term distance headway, ultra-short-term wind power, rockburst risk level and other things [ 19 – 21 ]. The convolutional structure of the TCN model is shown in Fig 1 .…”

Section: Methodologiesmentioning

confidence: 99%

Short-term power load forecasting based on the CEEMDAN-TCN-ESN model

Huang,

Zhang,

Jiang

2023

PLoS ONE

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Ensuring an adequate electric power supply while minimizing redundant generation is the main objective of power load forecasting, as this is essential for the power system to operate efficiently. Therefore, accurate power load forecasting is of great significance to save social resources and promote economic development. In the current study, a hybrid CEEMDAN-TCN-ESN forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and higher-frequency and lower-frequency component reconstruction is proposed for short-term load forecasting research. In this paper, we select the historical national electricity load data of Panama as the research subject and make hourly forecasts of its electricity load data. The results show that the RMSE and MAE predicted by the CEEMDAN-TCN-ESN model on this dataset are 15.081 and 10.944, respectively, and R2 is 0.994. Compared to the second-best model (CEEMDAN-TCN), the RMSE is reduced by 9.52%, and the MAE is reduced by 17.39%. The hybrid model proposed in this paper effectively extracts the complex features of short-term power load data and successfully merges subseries according to certain similar features. It learns the complex and varying features of higher-frequency series and the obvious regularity of the lower-frequency-trend series well, which could be applicable to real-world short-term power load forecasting work.

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Section: Methodologiesmentioning

confidence: 99%

Short-term power load forecasting based on the CEEMDAN-TCN-ESN model

Huang,

Zhang,

Jiang

2023

PLoS ONE

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“…Chen et al [31] constructed a data-driven model for rock bursts with regard to convolutional neural networks and deep learning and calculated the rock burst probability for each corresponding level. Ma et al [32] used the LightGBM algorithm and correlation coefficient heat map to screen the characteristic variables of rock bursts and used the random forest classification model to classify and predict the rock burst risk level at the "t + 1" moment. Ji et al [33] embraced the genetic algorithms to optimize the parameters of a support vector machine (SVM) and established a GA-SVM model to predict the rock burst using microseismic monitoring data.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model

Yuan,

Ji,

Liu

et al. 2023

Applied Sciences

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In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and feature information extraction from 10 main factors that affect the occurrence of rock bursts. On this basis, six principal component elements of the influencing factors of rock bursts have been obtained as the input vectors for an extreme learning machine (ELM). In the meantime, the parameter optimization ability of the PSO algorithm was adopted, the input weight values of the ELM and the threshold values of the hidden layer were optimized, and the functions of the three models were completely combined. Therefore, an early warning model of rock bursts based on the PCA-PSO-ELM combined algorithm was creatively proposed and the risk rank of rock bursts in the Yanshitai Coal Mine was predicted and evaluated. Consequently, the research results indicated that the prediction accuracy of the PCA-PSO-ELM model improved the prediction performance and generalization ability and reached a 100% contrast with the three models, namely the BP neural network, the radial basis function, and the extreme learning machine, which presented an updated method for the early warning investigation of rock burst disasters and had favorable engineering significance.

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“…The first one is the machine learning algorithm. Many machine learning algorithms have been applied to analyze rockburst hazards, such as decision trees [27], ensemble learning [28] and artificial neural networks [29]. Machine learning algorithms can build a complex and nonlinear relationship between indicators and hazard levels based on existing rockburst cases.…”

Section: Introductionmentioning

confidence: 99%

Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information

Li,

Liang,

Zhao

2023

Applied Sciences

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Rockburst is a major disaster in deep mining engineering, which can cause serious injury, death and economic losses. This study aims to solve rockburst hazard evaluation problems and determine the hazard levels of mines under uncertain circumstances. To this end, a novel multicriteria decision-making (MCDM) method was proposed in an interval-valued fuzzy context. The main contributions are three-fold. First, considering the heterogeneity of rock masses and the complexity of mining engineering, interval-valued fuzzy numbers (IVFNs) were adopted to express initial indicator information. Second, accounting for the uncertainty of indicator weights, the decision-making trial and evaluation laboratory (DEMATEL) and entropy methods were extended with IVFNs to determine fuzzy indicator weights comprehensively. Third, the complex proportional assessment (COPRAS) approach was extended with IVFNs to determine the rockburst hazard level. Finally, the proposed method was applied to evaluate rockburst hazards in the Jiaojia gold mine, and the ranking results were consistent with field status. Meanwhile, sensitivity and comparison analyses were performed to reveal the stability and effectiveness of the proposed method. The results indicated that the extended COPRAS method was reliable for rockburst hazard evaluation in deep mining engineering.

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Research on Rockburst Risk Level Prediction Method Based on LightGBM−TCN−RF

Cited by 8 publications

References 8 publications

Short-term power load forecasting based on the CEEMDAN-TCN-ESN model

Short-term power load forecasting based on the CEEMDAN-TCN-ESN model

Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model

Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information

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