Mining of Mineral Deposits

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Prediction of underground mining-induced subsidence: Artificial neural network based approach

Long Quoc Nguyen1,2, Tam Thanh Thi Le1,3, Trong Gia Nguyen1,3, Dinh Trong Tran4

1Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi, Vietnam

2Innovations for Sustainable and Responsible Mining, Hanoi University of Mining and Geology, Hanoi, Vietnam

3Geodesy and Environment, Hanoi University of Mining and Geology, Hanoi, Vietnam

4Department of Geodesy and Geomatics Engineering, Hanoi University of Civil Engineering, Hanoi, Vietnam


Min. miner. depos. 2023, 17(4):45-52


https://doi.org/10.33271/mining17.04.045

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      ABSTRACT

      Purpose. Mining-induced land subsidence is a significant concern in areas with extensive underground mining activities. Therefore, the prediction of land subsidence is crucial for effective land management and infrastructure planning. This research applies an artificial neural network (ANN) to predict land subsidence over the Mong Duong underground coal mine in Quang Ninh, Vietnam

      Methods. In the ANN model proposed in this research, four features are used as the model inputs to predict land subsi-dence, i.e., model outputs. These features include the positions of ground points in the direction of the trough main cross-section, the distance from the chamber (goaf) center to the ground monitoring points, the accumulated exploitation volume of extraction space, and the measured/recorded time. The entire dataset of 12 measured epochs, covering 22 months with a 2-month repetition time period, is divided into the training set for the first 9 measured epochs and the test set for the last 3 measured epochs. k-fold cross-validation is first applied to the training set to determine the best model hyperparameters, which are then adopted to predict land subsidence in the test set.

      Findings. The best model hyperparameters are found to be 5 hidden layers, 64 hidden nodes and 240 iterated epochs. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the predicted land subsidence depend on the time separated between the last measured epoch and the predicted epoch. Within 2 months from the last measurements, RMSE and MAE are at 22 and 13 mm for Epoch 10, which increase to 31 and 20 mm for Epoch 11 (4 months from the last measurement) and 37 and 24 mm for Epoch 12 (6 months from the last measurement).

      Originality. A new ANN model with associated “optimal” hyperparameters to predict underground mining-induced land subsidence is proposed in this research.

      Practical implications. The ANN model proposed in this research is a good and convenient tool for estimating mining-induced land subsidence, which can be applied to underground mines in Quang Ninh province, Vietnam.

      Keywords: subsidence prediction, underground mine, machine learning, artificial neural network (ANN)


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