Source Discrimination of Mine Water by Applying the Multilayer Perceptron Neural Network (MLP) Method—A Case Study in the Pingdingshan Coalfield

Abstract

In a complex multiaquifer mine, discriminant approaches that have previously been presented cannot successfully locate water sources. With multiple processing layers, computing models may learn representations of data at various levels of abstraction. Modern technology in many domains has significantly enhanced these models. 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 multilayer perceptron neural network (MLP) method to discriminate the source of the mine water. There were five types of mine water in the Pingdingshan coalfield. Each type of water was encoded with the numbers 0 to 4. The one-hot code method was used to encode the numbers, which is the output set. On the basis of hydrochemical data processing, the MLP model was developed using a characteristic ion contrast on aquifers with distinctive chemical properties. The research results show that two hidden layers (with 10 neurons in each hidden layer) for the model were fit for completing the prediction process with a better performance. This approach enabled us to discriminate water sources for the Pingdingshan coalfield and could be tried for other coalfields with similar hydrogeological conditions.