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

Abstract

Mine water inrush disaster seriously threatens the production of coal mine. Rapid and accurate identification of mine water inrush sources is a key premise for mine water disaster prevention. The conventional research on the identification of water inrush source has focused on a single source, and the identification of mixed water samples from multi-source aquifers in deep coal mining environment is not yet fully explored. In this study, absorption spectrum technology was introduced into the identification of water inrush sources. The absorption spectra of the water samples with different mixing ratios were prepared using the ultraviolet and visible spectrophotometry (UV–Vis) spectrophotometer. In addition, spectral data preprocessing such as scattering correction, baseline correction, smoothing and denoising, and data enhancement were conducted to reduce the influence of experimental error, environment, radiation, molecular interaction, and other factors on the spectral data. Furthermore, a genetic algorithm (GA) was used to improve the seven parameters of the extreme gradient boosting (XGBoost) algorithm, such as learning rate, base model selection, tree parameters, regularization parameters, and iteration times. The deep-learning classifier of mine mixed water sources based on GA-XGBoost was established and used to identify 66 groups of mixed water sources in the Huangyuchuan Mine. The simulation results show that spectral preprocessing and normalization enhancement effectively improved the accuracy of the discriminant model. After 100 cross-validations, the average recognition accuracy of the GA-XGBoost model was 94%, and the results were accurate and reliable. This study provides a new direction and method for the identification of water inrush sources, particularly for mixed water inrush sources. It may also serve as a technical reference for decision-makers to formulate effective coal mine water inrush prevention and control programs and for mine water disaster prevention in similar coalfields in North China.

Mine water inrush source discrimination model based on KPCA-ISSA-KELM

“…WEN Tingxin et al [9] established a mine water inrush source identification model based on KPCA-PSO-RBF-SVM by applying Kernel Principal Component Analysis (KPCA) to extract features and optimizing the kernel parameters and penalty factor of Support Vector Machines (SVM) by combining the PSO and Radial Basis Function (RBF), which provides a new way of identifying the water inrush source. LI Lin [10] proposed a Random Forest (RF) mine water inrush source identification method based on feature importance, which is quickly and accurately able to identify water inrush source even in the absence of water source data. JU Qiding et al [11] established a water inrush source identification model combining PCA and Bayesian Discriminant (Bayes), which improved the safety of Pan'er coal mine and provided a theoretical reference for mine water prevention and control work in similar mines.…”

Identification of Limestone Aquifer Inrush Water Sources in Different Geological Ages Based on Trace Components

“…In recent years, with the development of big data and artificial intelligence, machine learning has been applied to more and more industries [12,13]. Many experts and scholars at home and abroad have applied machine learning models such as the neural network method [14,15], support vector machine [16] and particle swarm optimization [17] to the field of water source identification. The machine learning model has obvious advantages when dealing with a large amount of data, but insufficient advantages in its recognition accuracy and the processing speed of a small amount of data, so the traditional water source identification method cannot be completely replaced.…”

Research on the Mechanism and Evolution Law of Delayed Water Inrush Caused by Fault Activation with Mining

“…The multi-field evolution law of fault activation inducing water inrush is relatively clear, and the prevention and control technologies have obtained certain field effects. Numerous other scholars, aiming at water inrush of confined water, carried out numerous studies to guide in resource mining safety [17][18][19][20].…”