Abstract
When mine water inrush accidents occur, timely and accurately identifying the water inrush source plays an important role in determining the cause of water inrush and making a solution to a disaster. According to the differences of water chemical composition in each water sources of mine, eight kinds of indicators of water chemical composition were selected as sample variables for water inrush source identification. On this basis, an identification model of water inrush source was established by using principal component analysis (PCA) and Fisher discriminant analysis (FDA) combined. The model was used to identify the water inrush source of 14 groups of training samples and 12 groups of samples to be judged in different water sources of the Xiandewang coal mine, and it was compared with the results of the conventional identification model which used the FDA method. Results of this study showed that having processed data by using the PCA method can effectively eliminate the effects of information superposition between sample indicators, and the identification accuracy of mine water inrush source was significantly increased. Related study in this paper can provide some basis and reference for the study of mine water inrush source identification technology.
Highlights
This paper introduced the principal component analysis (PCA) method into the water inrush source identification technology, refined the water chemical indicator data of different water sources, converted multiple related indicator variables into a new independent one by linear combination, and eliminated the effects caused by information superposition between indicators so that characteristics of different water sources can be described more effectively
PCA is aimed at converting a set of potentially correlated variables to a new set of variables that are linearly uncorrelated by means of orthogonal transformation; and the new variables obtained through transformation are known as principal components and they are capable of keeping the original information to be revealed unchanged in the aspect of expressing information
According to the characteristics of mine water inrush source, the water source identification analysis model based on PCAFDA and the conventional water source identification analysis model based on Fisher discriminant analysis (FDA) were used to identify the water inrush source of the 14 groups of training samples and 12 groups of samples to be judged, respectively, and the identification accuracies were 85.7% and 78.6%, 91.7% and 75% correspondingly
Summary
Liu et al (2015) established a water source identification model based on the BP neural network theory and randomly selected water samples collected during mine excavation to predict water source identification These methods above have a positive effect on enriching the water inrush source identification technology, but they did not take information superposition between identification indicators of water chemical into consideration, which caused problems like low precision of classification and long response time. This paper introduced the principal component analysis (PCA) method into the water inrush source identification technology, refined the water chemical indicator data of different water sources, converted multiple related indicator variables into a new independent one by linear combination, and eliminated the effects caused by information superposition between indicators so that characteristics of different water sources can be described more effectively On this basis, the Fisher discrimination analysis (FDA) method was combined to establish a water source identification analysis model. The water inrush source of the typical coal mine was identified and the results of identification were good
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