Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area

Abstract

Source discrimination of mine water plays an important role in guiding mine water prevention in mine water management. To accurately determine water inrush source from a mine in the Jiaozuo mining area, a Piper trilinear diagram based on hydrochemical experimental data of stratified underground water in the area was utilized to determine typical water samples. Additionally, principal component analysis (PCA) was used for dimensionality reduction of conventional hydrochemical variables, after which mutually independent variables were extracted. The Piper-PCA-Fisher water inrush source recognition model was established by combining the Piper trilinear diagram and Fisher discrimination theory. Screened typical samples were used to conduct back-discriminate verification of the model. Results showed that 28 typical water samples in different aquifers were determined through the Piper trilinear diagram as a water sample set for training. Before PCA was carried out, the first five factors covered 98.92% of the information quantity of the original data and could effectively represent the data information of the original samples. During the one-by-one rediscrimination process of 28 groups of training samples using the Piper-PCA-Fisher water inrush source model, 100% correct discrimination rate was achieved. During the prediction and discrimination process of 13 samples, one water sample was misdiscriminated; hence, the correct prediscrimination rate was 92.3%. Compared with the traditional Fisher water source recognition model, the Piper-PCA-Fisher water source recognition model established in this study had higher accuracy in both rediscrimination and prediscrimination processes. Thus it had a strong ability to discriminate water inrush sources.

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

“…Abnormal water chemical compositions in the same aquifer cannot reflect the hydrochemical characteristics of underground water in this aquifer. We therefore had to identify the water sample that best represented the aquifer water chemical composition and establish a high-precision water inrush water source discrimination model [25].…”

“…The application of these mathematical and machine learning methods enriches the content of mine water source discrimination theory, improves identification accuracy, and demonstrates good practicability and effectiveness. However, most of these present discriminant methods have not considered the complicated information superposition problem between water chemistry indicators, a problem that results in misdiscrimination of the established model in the practical application process, and their recognition accuracy still needs to be further improved [25]. Therefore, some scholars have adopted the principal component analysis (PCA) method in the water source discriminant analysis and obtained better analysis results [26].…”

Quantitative Evaluation of Karst Developmental Differences Based on Improved Analytic Hierarchy Process and Multidimensional Extension Matter Element Model

“…Determining the weight value of each index factor is the key to evaluate the degree of karst development, and the selection of the most appropriate methodology is crucial for ensuring the accuracy of the identification results. At present, the methods commonly used to determine weights are the analytic hierarchy process and the entropy weight method [25][26][27].…”

Study on Hydrochemical Characteristics of Ordovician Limestone in Jiaozuo Mining Area

“…It is the most widely applied method in hydrogeochemical research at present, and can explain many hydrogeochemical problems (e.g. [13] ~ [14]). The main ions in common components of groundwater are Na + +K + , Ca 2+ , Mg 2+ , HCO3 -, Cl -, SO4 2and CO3 2-.…”