Source identification of mine water inrush based on the exponential whitenization function and the grey situation decision model

Abstract

Coal mining at deep levels can cause mine water inrush and groundwater contamination, making it important to accurately and rapidly identify the water inrush source. In this study, 52 water samples were extracted from three types of aquifers in the Linhuan mining area, China. The water sample components Na+ + K+, Ca2+, Mg2+, HCO3−, Cl−, and SO42−, measured in the experiment, were used as evaluation variables, and the piecewise function equation was established by using the exponential whitening function. Finally, combined with water sample data and the CRITIC weighted grey situation decision-making method, the comprehensive membership degree was obtained, and the water inrush source was identified according to the principle of the maximum membership degree. The comprehensive accuracy of the model was 92.3%. The traditional grey situation decision-making method uses the linear whitening function to determine the membership value, ignoring that the value of the whitening function outside the adjacent level is 0, which improves the weight of the adjacent level, causes the loss of effective information, and reduces the discrimination rate. The exponential whitenization function in this paper will solve this problem and further improves the grey situation decision-making method to discriminate the water inrush source, which would also be beneficial regarding the prevention and control of mine water inrush and groundwater contamination.

The Efficiency of Economic Growth for Sustainable Development—A Grey System Theory Approach in the Eurozone and Other European Countries

“…The developed methodology involves defining whitening functions, through which it becomes possible to determine a specific value for the synthetic indicator of sustainable economic growth. Determining the indicator in question can be conveyed as a research procedure consisting of seven distinct steps [26].…”

“…Within many methods of this theory, there emerges a need to transform grey numbers into white numbers. This process is known as the whitening of grey numbers [26]. There are two main approaches to achieve whitening.…”

Identification of Mine Mixed Water Inrush Source Based on Genetic Algorithm and XGBoost Algorithm: A Case Study of Huangyuchuan Mine

“…By combining data from water chemical characteristics and physical characteristics, such as temperature and water level, researchers have been able to determine the water sources in mine water inrush disasters [21][22][23][24][25][26][27]. In recent years, more inroads have been made for increasing the speed and accuracy when identifying these sources by using a variety of mathematical model calculation methods based on the characteristics of water chemical physics, as well as other factors [28][29][30][31][32]. Based on hydrochemical data and self-organizing feature maps (SOM), Zhao et al [28] analyzed the water inrush source of Ningtiao mine.…”

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

“…To e ectively guarantee the safe production of coal mines and reduce the occurrence of mine water disasters, it is an important prerequisite for the prevention and control of water disasters to accurately identify the types of mine water inrush sources [5,6]. At present, there are many methods used to distinguish mine water inrush sources, such as groundwater level dynamics [7], hydrochemical analysis [8], GIS theoretical analysis [9], and so on [10]. Among them, the hydrochemical analysis method is one of the most widely used method [11], but the hydrochemical method needs to obtain the hydrochemical parameters of the mine water source (such as pH value, ion concentration, and conductivity) in the harsh experimental environment.…”