Currently, there is a contradiction between coal mining and protection of water resources, meaning that there is a need for an effective method for discriminating the source of mine gushing water. Ningtiaota Coal Mine is a typical and representative main coal mine in the Shennan mining area. Taking this coal mine as an example, the self-organizing feature map (SOM) approach was applied to source discrimination of mine gushing water. Fisher discriminant analysis, water temperature, and traditional hydrogeochemical discrimination methods, such as Piper and Gibbs diagrams, were also employed as auxiliary indicators to verify and analyze the results of the SOM approach. The results from the three methods showed that the source of all the gushing water samples was surface water. This study represents the innovative use of an SOM in source discrimination for the first time. This approach has the advantages of high precision, high efficiency, good visualization, and less human interference. It can quantify sources while also comprehensively considering their hydrogeochemical characteristics, and it is especially suitable for case studies with large sample sizes. This research provides a more satisfactory solution for water inrush traceability, water disaster prevention and control, ecological protection, coal mine safety, and policy intervention.
Mine water inrush is a major type of disaster in coal mine production in China, it causes heavy casualties and serious economic losses and threatens coal mine safety. To quickly and accurately identify mine water inrush source, according to the hydrochemical characteristics of different aquifers in the Donghuantuo mining area, this paper systematically analyzes the hydraulic connection of the aquifers in main coal mining areas before and after mining activities. Collected four types of hydrochemical data of No. 5 coal seam roof water, No. 8 coal seam roof water, No. 122 coal seam floor water, and No. 1214 coal seam aquifer water in the Donghuantuo mining area. In addition, based on the hydrochemical data, the parameter selection of LightGBM was optimized by Particle Swarm Optimization (PSO) and constructed the PSO-LightGBM water inrush source identification model. The recognition accuracy of PSO-LightGBM model was compared with LightGBM model, classification regression tree (CART) model, and random forest (RF) model. The results showed that coal mining activities would have a significant impact on the water quality characteristics of the roof sandstone fissure water of No. 5 coal mine. Mining activities had a certain impact on the accuracy of the identification model. In addition, compared with the four recognition models, PSO-LightGBM model had the highest recognition accuracy of 97.22%. It showed that the model had high accuracy, stability, generalization ability, and important reference value for the identification of mine water inrush source.
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