Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

• Main Authors: Ryszard Hejmanowski, Wojciech T. Witkowski
• Format: Article
• Language: English
• Published: Central Mining Institute (Główny Instytut Górnictwa) 2015-01-01
• Series: Journal of Sustainable Mining
• Subjects: Subsidence; Drainage of underground mine; Artificial neural networks; MLP
• Online Access: http://www.sciencedirect.com/science/article/pii/S2300396015000154

Based on the previous studies conducted by the authors, a new approach was proposed, namely the tools of artificial intelligence. One of neural networks is a multilayer perceptron network (MLP), which has already found applications in many fields of science. Sequentially, a series of calculations was made for different MLP neural network configuration and the best of them was selected. Mean square error (MSE) and the correlation coefficient R were adopted as the selection criterion for the optimal network. The obtained results were characterized with a considerable dispersion. With an increase in the amount of hidden neurons, the MSE of the network increased while the correlation coefficient R decreased. Similar conclusions were drawn for the network with a small number of hidden neurons. The analysis allowed to select a network composed of 24 neurons as the best one for the issue under question. The obtained final answers of artificial neural network were presented in a histogram as differences between the calculated and expected value.