An LSTM-based model for evaluation of the risk of ground collapse in coal mining area

Yongli Xie; Zhen Chen; Xiaoyong Yang; Yanfei Bai; Zeyu Wen

doi:10.1117/12.2615098

15 October 2021 An LSTM-based model for evaluation of the risk of ground collapse in coal mining area

Yongli Xie, Zhen Chen, Xiaoyong Yang, Yanfei Bai, Zeyu Wen

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119332C (2021) https://doi.org/10.1117/12.2615098
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

In the process of coal mining, it is easy to cause geological disasters such as ground collapse, which is particularly important for the evaluation and prediction of the risk of ground collapse in the mining area in order to reduce the losses caused by ground collapse. Ground collapse is affected by geology, hydrology, weather and other factors, the paper puts forward the risk evaluation model of ground collapse in coal mining area based on LTMS, takes 10 characteristics of pretreated mining area collapse as LSTM model input, according to the input data quantity, selects the number of samples per batch after experiments is 8, and selects Adam as the optimization function by SGD, RMSprop and Adam, and the number of hidden layer nodes is set to 30. The LSTM model achieves optimal performance at dropout value 0.5. The output of the model is divided into four levels of risk evaluation of the mining area, so as to construct the risk evaluation model of ground collapse in the mining area based on LSTM. It provides a new feasible way to evaluate the risk of mining goaf.

Citation Download Citation

Yongli Xie, Zhen Chen, Xiaoyong Yang, Yanfei Bai, and Zeyu Wen "An LSTM-based model for evaluation of the risk of ground collapse in coal mining area", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119332C (15 October 2021); https://doi.org/10.1117/12.2615098

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