Research On The Prediction Method Of The Risk Of Gushing (Sudden) Water In The Coal Bed Roof Aquifer Based On Machine Learning

In order to solve the problem of gushing(sudden)water in the root aquiter during the mining of shallow buried coal seams in northern Shaanxi Province,machine learning algorithms are applied to predict the risk of gushing(sudden)water in the roof aquifer based on the theory of "three maps-double prediction" to improve the prediction accuracy and provide a strong basis for the prevention and control of water hazards in the roof aquifer of coal seams in northern Shaanxi mining enterprises,the main research contents and results of this paper are as follows.(1)through the analysis of shallow buried coal seam roof aquifer gushing(sudden)water mechanism,it is concluded that the main water source of coal seam roof aquifer is weathered bedrock water,and its risk of gushing(sudden)water is mainly determined by the water-richness of the weathered bedrock of the aquifer and the height of water-conducting fissure zone development together,and analyze the main influencing factors of the water-richness of the weathered bedrock and the height of water-conducting fissure zone development.(2)Construction of water-richness prediction model for weathered bedrock of aquifers.Firstly,we select features based on variance filtering and cross-recursive feature elimination algorithm for aquifer water-richness influencing factors,and construct advanced features using a comparison experiment was conducted with other models using the borehole measured dataset,and after a five-fold cross-validation,the results showed that the prediction accuracy of the fusion model was improved by 2.8%.(3)Construction of a hydraulic fracture zone height prediction model.The hydraulic fissure zone development process is viewed as a time series problem,and the features are extracted from the hydraulic fissure zone data using a one-dimensional convolutional neural network(CNN),and then the extracted features are used to train a long and short term memory network(LSTM),while the LightGBM model is trained using the hydraulic fissure zone data,and the prediction results of the LSTM and LightGBM models are adjusted based on the inverse of the prediction error The weights of the predicted results of the LSTM and LightGBM models are adjusted based on the inverse of the prediction error to obtain the predicted height of the hydraulic fracture zone development.The experimental results show that the Mean Absolute Percentage Error(MAPE)and Root Mean Square Error(RMSE)of this model are reduced by 0.41 and 0.0822 compared with other models,which verifies the high accuracy of this model.(4)Model application.Using the prediction model constructed in this paper,the water-richness of the weathered bedrock of the top slab and the height of the hydraulic fracture zone are predicted in the adjacent mine area where no pumping borehole experiments are conducted,and the water-richness zoning map and the safety zoning map of the top slab fracture(hydraulic fracture zone)are obtained.Based on ArcGIS software overlaying the two maps,we obtained the water risk zoning map of the weathered bedrock of the water-bearing layer,which provides scientific guidance for water control work in shallow buried coal seam mining.