Classification of Water Source in Coal Mine Based on PCA-GA-ET

• Published in: Water (Basel) Vol. 15; no. 10; p. 1945
• Main Authors: Yang, Zhenwei, Lv, Hang, Wang, Xinyi, Yan, Hengrui, Xu, Zhaofeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21.05.2023
• Subjects: Accuracy; Algorithms; Aquifers; Artificial intelligence; calcium; Classification; coal; Coal industry; Coal mining; Comparative analysis; Genetic algorithms; hydrochemistry; Machine learning; Methods; Mines; Monte Carlo simulation; Mutation; Neural networks; principal component analysis; Principal components analysis; Support vector machines; trees; water
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w15101945

In recent years, inrush water has hampered the regular mining of coal mines, and the proper identification of the source of inrush water is critical to the prevention and management of water hazards in mines. This paper extracts the standard water chemistry discriminating ions Na++K+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− from observed water samples. An improved water source discrimination model is proposed which combines algorithms from data mining, classification models, and learning reinforcement. According to the Pearson correlation coefficient, Na++K+ has a strong correlation with HCO3−. To identify the major metrics, we performed principal component analysis (PCA), and the adaptive differential evolutionary genetic algorithm (GA) was utilized to optimize the depth of the extreme tree (ET) and the number of classifiers. Finally, the model distinguished 25 sets of studied samples from various water sources in the Pingdingshan coalfield. Comparative analysis demonstrated the efficacy of each stage of our work. PCA-GA-ET outperformed the conventional approaches, such as the support vector machine, BP artificial neural network, and random forest. The studies revealed that PCA-GA-ET can eliminate the information overlap between data and simplify the data structure and thereby improve the efficiency and accuracy of water source detection. We discovered that by utilizing the evolutionary algorithm to optimize parameters such as the depth of the extreme trees and the number of decision trees, we could get the model to converge faster and to be more stable and more accurate. The results suggest that PCA-GA-ET has good robustness and accuracy and can meet the needs of water source identification.