Valuating usability of artificial neural networks for subsidence prediction in underground coal mining

https://tapchi.humg.edu.vn/en/archives?article=653
  • Affiliations:

    1 Trường Đại học Mỏ - Địa chất, Việt Nam

  • Received: 30th-June-2016
  • Revised: 14th-Aug-2016
  • Accepted: 30th-Aug-2016
  • Online: 30th-Aug-2016
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Abstract:

This paper presents the results of assessing the artificial neural network usability to predict surface subsidence, caused by underground coal mining. In this paper, a 2-layer feedforward network are used. Training and testing data are taken from the subsidence forecast model that has been demonstrated to fit with geological - mining conditions in Quang Ninh coal seams. Assessment of predictability of the neural network after training period was conducted in 3 geological - mining conditions which are absolutely different from the training conditions. The largest differences between predicted and real values, corresponding to 3 cases of prediction, are 0.127m, 0.212m and 0.019m respectively. The largest RMS of 3 cases is 0.106, equivalent to 5% of maximum subsidence. This result is a premise to propose a neural network model for prediction of subsidence due to underground mining in Quang Ninh coal basin.

How to Cite
Nguyen, L.Quoc 2016. Valuating usability of artificial neural networks for subsidence prediction in underground coal mining (in Vietnamese). Journal of Mining and Earth Sciences. 55 (Aug, 2016).

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