Risk Evaluation Model of Coal Spontaneous Combustion Based on AEM-AHP-LSTM

Xu Zhou, Shangsheng Ren, Shuo Zhang, Jiuling Zhang () and Yibo Wang
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Xu Zhou: College of Science, North China University of Science and Technology, Tangshan 063210, China
Shangsheng Ren: School of Economics, North China University of Science and Technology, Tangshan 063210, China
Shuo Zhang: School of Basic Medical Sciences, North China University of Science and Technology, Tangshan 063210, China
Jiuling Zhang: School of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
Yibo Wang: College of Yisheng, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2022, vol. 10, issue 20, 1-16

Abstract: Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of coal spontaneous combustion. Firstly, the key indicators of coal spontaneous combustion were analyzed and used as risk factors to establish an evaluation system. Next, the objective and subjective weights were calculated using AEM and AHP, respectively. The objective and subjective weights were then combined, and TOPSIS was used to calculate the score of the evaluation sample. Finally, the obtained evaluation samples were trained with the BP, RBF, and LSTM model to resolve the problem of model overdependence on historical data and achieve the auto-adapt adjustment of weight with data change. Additionally, data from 15 typical Chinese coal mines were applied to the model. The results indicate that, compared with the BP and RBF neural networks, the LSTM model has higher prediction accuracy, stronger generalization ability, and stronger practicability. The modeling and application findings show that the AEM-AHP-LSTM model was better appropriate for the risk assessment of coal spontaneous combustion. This method can potentially be further applied as reliable approach for the assessment of mine disaster risk.

Keywords: coal spontaneous combustion; risk evaluation; AEM; AHP; LSTM; mine intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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