Mine Hydrological Parameter Analysis And Prediction Based On Deep Learning

At present,all coal mines have installed hydrological monitoring systems in accordance with the clear provisions of the "Coal Mine Safety Regulations" and have accumulated a large amount of hydrological data,but there are problems with insufficient data analysis capabilities and low utilization rates.This paper conducts an in-depth analysis of mine historical hydrological data,establishes a deep learning prediction model of hydrological parameters,provides technical support for preventing mine water damage,and is of great significance to ensure coal mine safety production.The hydrological parameter analysis is to provide the basis for the establishment of the later prediction model.The mine monitoring hydrological data has obvious characteristics of noise and non-linearity.The stationarity of the hydrological data is judged by the autocorrelation graph.The autocorrelation coefficient is always positive in a long delay period,indicating that it has typical non-stationarity.In addition,the correlation of multi-sequence hydrological parameters is analyzed by Pearson correlation coefficient and scatter diagram matrix.The results show that there are different degrees of correlation among different parameter sensors,and the data of each monitoring point with the same parameter also have correlation.The non-stationary characteristics of mine hydrological data make it difficult for traditional single-sequence forecasting methods to achieve effective forecasting results.In order to improve the prediction accuracy,the paper proposes the CEEMD GRU model.First,the hydrological data is decomposed into multiple stable sub-components by CEEMD;secondly,the number of lag periods of the hydrological data is determined by PACF,so as to determine the number of input neurons;and then the change law of each component is learned and predicted by the GRU neural network;Finally,the prediction results of each component are fused to obtain the final prediction value,and compared with the other five neural networks model based on two sets of data,the root mean square error of the test set is reduced by 36.38%and 25.48%on average.Single-series forecasts are modeled based on the autocorrelation of the data.Then,the different hydrological sensors in the mine are also spatially correlated.This paper uses this correlation to establish the GRU_Attention model.First,input the multi-dimensional sequence into the GRU to achieve high-level feature learning;then use the output of the GRU as the input of the attention mechanism,and use the attention mechanism to mine the relationship between the multi-dimensional input and output,and calculate the feature weight;finally,the GRU layer The weighted summation of output and feature weights enhances or weakens each input to obtain a feature expression vector,which is input to the fully connected layer to calculate the final predicted value.Comparing experiments with five other neural networks,the root mean square error of the test set is reduced by 31.1%.In this paper,the mine hydrological parameters are deeply analyzed,and the deep learning prediction model is built and verified by experiments.The results show that the model proposed in this paper has better prediction effect.Provide technical support for mine water inrush prevention and drainage system design.