Artificial Intelligence In Source Discrimination of Mine Water: A Deep Learning Algorithm For Water Source Discrimination
Abstract With increasing coal mining depth, the source of mine water inrush becomes increasingly complex. The problem of distinguishing the source of mine water in mines and tunnels has been addressed by studying the hydrochemical components of the Pingdingshan Coalfield and applying the artificial intelligence (AI) method to discriminate the source of the mine water. 496 data of mine water have been collected. Six ions of mine water are used as the input data set: Na++K+, Ca2+, Mg2+, Cl-, SO2- 4, and HCO- 3. The type of mine water in the Pingdingshan coalfield is classified into surface water, Quaternary pore water, Carboniderous limestone karst water, Permian sandstone water, and Cambrian limestone karst water. Each type of water is encoded with the number 0 to 4. The one-hot code method is used to encode the numbers, which is the output set. On the basis of hydrochemical data processing, a deep learning model was designed to train the hydrochemical data. Ten new samples of mine water were tested to determine the precision of the model. Nine samples of mine water were predicted correctly. The deep learning model presented here provides significant guidance for the discrimination of mine water.