Temporal Prediction Model of Water Inflow in Chengchao Iron Mine Based on Rainfall Data
The non-pillar sublevel caving mining will inevitably form a large-scale surface subsidence area.The distribu-ted water-conducting fractures will lead to a large amount of surface precipitation infiltrating downward,causing a sudden in-crease in roadway water inflow.Chengchao Iron Mine has now entered the mining level of-500 m.The interaction mechanism between atmospheric rainfall and the continuous dynamic development of fractures caused by caving mining is very complicat-ed.Therefore,in order to predict the underground water inflow scientifically and accurately,this paper proposes a grey GM(1,2)time series prediction model of water inflow with rainfall as input data.Taking the actual data of rainfall and water inflow in Chengchao Iron Mine from 2019 to 2021 as training samples,fully considering the continuous impact of caving mining on overlying rock mass,the time series coefficient K is introduced to optimize the model.Finally,the grey GM(1,2)time series prediction model is established.Compared with the traditional GM(1,2)prediction model,the prediction accuracy is improved by 7.79%on average.The model is used to predict the water inflow in 2022.The results show that the prediction accuracy of dry and rainy seasons is 93.51%and 93.58%respectively,and the prediction effect is good.The research results are an ef-fective method to directly predict the mine water inflow data through the surface rainfall data.
non-pillar sublevel caving methodwater inflow predictiongrey system theoryGM(1,2)modeltime series coefficient
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