Research on the prediction of mine water inrush disasters based on multi-factor spatial game reconstruction

Abstract

AbstractWater damage in mines poses a widespread challenge in the coal mining industry. Gaining a comprehensive understanding of the multi-factor spatial catastrophe evolution mechanism and process of floor water inrush is crucial, which will enable the achievement of dynamic, quantitative, and precise early warning systems. It holds significant theoretical guidance for implementing effective water prevention and control measures in coal mines. This study focuses on the issue of water inrush in the coal seam floor, specifically in the context of Pengzhuang coal mine. By utilizing a small sample of non-linear characteristics derived from drilling geological data, we adopt a multifactor spatial perspective that considers geological structure and hydrogeological conditions. In light of this, we propose a quantitative risk prediction model that integrates the coupled theoretical analysis, statistical analysis, and machine learning simulation methods. Firstly, the utilization of a quantification approach employing a triangular fuzzy number allows for the representation of a comparative matrix based on empirical values. Simultaneously, the networked risk transmission effect of underlying control risk factors is taken into consideration. The application of principal component analysis optimizes the entropy weight method, effectively reducing the interference caused by multifactor correlation. By employing game theory, the subjective and objective weight proportions of the control factors are reasonably allocated, thereby establishing a vulnerability index model based on a comprehensive weighting of subjective and objective factors. Secondly, the WOA-RF-GIS approach is employed to comprehensively explore the interconnectedness of water diversion channel data. Collaborative Kriging interpolation is utilized to enhance the dimensionality of the data and facilitate spatial information processing. Lastly, the representation of risk is coupled with necessary and sufficient condition layers, enabling the qualitative visualization of quantitative results. This approach aims to accurately predict disaster risk with limited sample data, ultimately achieving the goal of precise risk assessment. The research findings demonstrate that the reconstructed optimization model based on multi-factor spatial game theory exhibits high precision and generalization capability. This model effectively unveils the non-linear dynamic processes associated with floor water inrush, which are influenced by multiple factors, characterized by limited data volume, and governed by complex formation mechanisms. The identification of high-risk areas for water inrush is achieved with remarkable accuracy, providing invaluable technical support for the formulation of targeted water prevention and control measures, ultimately ensuring the safety of coal mining operations.