Recognition of mine water-inrush sources using artificial neural network

In the process of excavating and mining, water-inrush episodes induced by a number of geological or human factors is a complex geological hazard and often lead to disastrous consequences, making an accurate prediction before an inrush accident is difficult because there are so many factors and interactions between factors are related in such hazard, No matter how accurate a risk assessment approach is, it can not 100% guarantee that every water inrush accident can be accurately predicted. Until so far, inrush accidents are still occurring every year all over the world, especially in developing countries. For inrush accidents in underground mining, the first and also the critical step of controlling the accident is to find out the related inrush sources, accurately identifying which aquifer or which water body is directly related to the inrush accident is the critical step of controlling water volume and reducing casualties and economic losses. In this study, method of using artificial neural network (ANN) to identify the water-inrush sources is proposed, by establishing a back propagation neural network (BPNN) to train, test and predict the sample data selected from Jiaozuo mine area, results show that ANN is an effective approach to identify water sources.