## 1 Introduction  
Coal, as the primary energy source in China, plays an essential role in economic and social development [1]. The Jurassic coal fields in northern Shaanxi Province represent a favorable development area for China's coal resources, contributing significantly to the country's energy strategy. However, with the high-intensity extraction of coal resources, water hazards in coal mines have become frequent, resulting in significant losses to public safety and national resources [2, 3]. The Ningtaiota Minefield is located within the Jurassic coal fields in the northern region of Shaanxi Province, with an annual production capacity of 12 Mt/a. The working face S1210 is the first working face in the southern flank, approximately 6000 m in length and 300 m in width. On 30 May 2011, when the working face advanced to a depth of 61 m, a water inrush accident occurred at the roof, with an initial water inflow of up to 1300 m\({}^{3}\)/h [4]. It is inferred that the primary source of this water inrush was the weathered bedrock aquifer [5]. This aquifer exhibits varying water-bearing capacities, ranging from low to high, with uneven spatial distribution, posing a severe threat to the safe operation of the mine [6]. Therefore, it is crucial to study the water-bearing properties of the weathered bedrock aquifer in the southern flank of the Ningtiaota Minefield to guide the safe mining of the remaining working faces in the southern flank. Significant work has been undertaken by researchers on methods for evaluating water-bearing properties. The pumping test method [7; 8; 9] is the main traditional method for evaluating the water-bearing properties of aquifers. However, most conventional water-bearing properties evaluation methods rely on relatively scarce hydrogeological boreholes. The high cost of these boreholes and their limited number result in insufficient coverage of the overall area, making it difficult to accurately reflect the uneven spatial distribution of water-bearing properties in aquifers. Therefore, it is very worthwhile to explore the establishment of a water-bearing properties evaluation system using the widely available geological exploration data in coal mines. In recent years, with the rise of predictive models and computer machine learning, multifactor prediction methods have gradually become the mainstream approach for evaluating the water-bearing properties of aquifers. [PERSON], an academic at the China Academy of Engineering, proposed the \"Three Maps--Double Prediction Method\" for predicting the water inrush (or outburst) conditions of a roof plate through water-bearing zoning and rupture safety zoning [10]. [PERSON] et al. selected the depth of the sandstone floor, brittle rock ratio, lithological structure index, fault strength index, and fault intersections and endpoints density as the main controlling factors and proposed a combined weighting method based on AHP-RS-MD to evaluate the water-bearing properties of a clastic rock aquifer of the coal seam roof [11]. The multifactor integrated evaluation method can assess water-bearing properties under the conditions of complex indicators and offer the advantages of being rapid and low-cost [12]. As a multifactor evaluation method, the Bayes discriminant has a wide range of practical applications in machine learning and has been gradually applied by scholars in water-bearing property prediction in recent years [13]. However, in traditional Bayes discriminant, it is necessary to consider whether each variable meets the multivariate normal distribution, which can lead to suboptimal classification results [14]. Therefore, this paper proposes using the kernel density estimation method to estimate the kernel density of the main control factors in the study areas that do not meet the normal distribution, solving the problem that the main control factors affecting the water-bearing properties fail to meet the normal distribution.